Archive for category Rehabilitation robotics

[Abstract + References] Kineto-static Analysis of a Compact Wrist Rehabilitation Robot Including the Effect of Human Soft Tissue to Compensate for Joint Misalignment


Developing a simple, comfortable rehabilitation robot that can carry out in-home rehabilitation has been a long-time challenge. In this paper, we present a rehabilitation robot with one degree of freedom (DOF) for wrist joint flexion-extension movement. Passive joints have been added to the exoskeleton, forming a four-bar slider crank mechanism, which can reduce unwanted forces due to joint misalignment. A concept of modeling human soft tissue as a passive prismatic joint with spring is introduced in order to achieve the compactness and comfort of the robot simultaneously. In addition, the effects of human soft tissue displacement are compared. A trade-off between robot volume and comfort is discussed. Finally, the kineto-static analysis of the proposed design is conducted to prove the feasibility of adopting this concept in robot-assisted rehabilitation.


  1. 1.Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(1), 3–31 (2014)CrossRefGoogle Scholar
  2. 2.Norouzi-Gheidari, N., Archambault, P.S., Fung, J.: Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: Systematic review and meta-analysis of the literature. J. Rehabil. Res. Dev. 49(4), 479–496 (2012)CrossRefGoogle Scholar
  3. 3.Ryu, J., Cooney, W.P., Askew, L.J., An, K.-N., Chao, E.Y.S.: Functional ranges of motion of the wrist joint. J. Hand Surg. Am. 16(3), 409–419 (1991)CrossRefGoogle Scholar
  4. 4.Pezent, E., Rose, C.G., Deshpande, A.D., O’Malley, M.K.: Design and characterization of the openwrist: A robotic wrist exoskeleton for coordinated hand-wrist rehabilitation. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 720–725 (2017)Google Scholar
  5. 5.McDaid, A.J.: Development of an Anatomical Wrist Therapy Exoskeleton (AW-TEx). In: 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 434–439 (2015)Google Scholar
  6. 6.Singh, N., Saini, M., Anand, S., Kumar, N., Srivastava, M.V.P., Mehndiratta, A.: Robotic exoskeleton for wrist and fingers joint in post-stroke neuro-rehabilitation for low-resource settings. IEEE Trans. Neural Syst. Rehabil. Eng. 27(12), 2369–2377 (2019)CrossRefGoogle Scholar
  7. 7.Näf, M.B., Junius, K., Rossini, M., Rodriguez-Guerrero, C., Vanderborght, B., Lefeber, D.: Misalignment compensation for full human-exoskeleton kinematic compatibility: State of the art and evaluation. Appl. Mech. Rev. 70(5), 1–19 (2019)Google Scholar
  8. 8.Liu, Y.-C., Takeda, Y.: Static analysis of a wrist rehabilitation robot with consideration to the compliance and joint misalignment between the robot and human hand. In: Proceedings of Annual Conference of the Robotics Society of Japan 2019, Tokyo (2019)Google Scholar
  9. 9.Liu, Y.-C., Takeda, Y.: Kineto-static analysis of a wrist rehabilitation robot with compliant elements and supplementary passive joints to compensate the joint misalignment. In: The 25th Jc-IFToMM Symposium, Japan (2019)Google Scholar
  10. 10.Xiao, Z.G., Menon, C.: Towards the development of a portable wrist exoskeleton. In: 2011 IEEE International Conference on Robotics and Biomimetics, pp. 1884–1889 (2011)Google Scholar
  11. 11.Takeda, Y., Sugahara, Y., Matsuura, D., Matsuda, S., Suzuki, T., Kitagawa, M., Liu, Y.-C.: Introduction of dynamic pair to modeling and kinemato-dynamic analysis of wearable assist-devices. In: The JSME Annual Mechnical Engineering Congress 2019, Akita, Japan (2019)Google Scholar
  12. 12.Yu, T.F., Wilson, A.J.: A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model. Comput. Methods Programs Biomed. 114(3), e46–e59 (2014)CrossRefGoogle Scholar


, , , , , ,

Leave a comment

[ARTICLE] Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control – Full Text


Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.


Science and technology and people’s living standards are gradually improving, whether it is China or other countries in the world, and these changes will bring about an aging population problem. In recent years, due to the impact of cardiovascular and cerebrovascular diseases, there have been some changes in middle-aged and elderly patients with hemiplegia. The number of patients has increased and the trend of becoming younger. At the same time, on the other hand, due to the rapid growth of the number of transportation vehicles, more and more The more people suffer from nervous system injuries or limb injuries due to traffic accidents [1]. Strictly speaking, according to medical theory and clinical medicine, in addition to early surgical treatment and necessary medical care, correct and scientific rehabilitation education is also very important for the recovery and improvement of limb motor ability, but these patients have exercise Obstacles, can’t do rehabilitation training alone, and someone needs help, but in view of the fact that there are not enough medical staff in our country, these patients will be in an embarrassing situation. In this respect, the development of a remotely controlled upper limb rehabilitation robot is of great significance for solving the problem of unattended patients with hemiplegia.

Sanja Vukićević once designed a robust controller of a two-degree-of-freedom upper limb rehabilitation robot for the motion characteristics of rehabilitation training and the inherent properties of the robot, so that the robot can drive the precise trajectory of hemiplegic patients according to the given trajectory, ensuring Under the system dynamics model with zero error, the modeling error bounded error remains consistent and bounded, and the tracking error is zero. The simulation results of Sanja Vukićević show that the robust control strategy can make the system tracking error tend to under certain conditions Zero, has a good control effect, although Sanja Vukićević’s method improves the robustness of rehabilitation training robots, but the reliability has decreased [2][3]. Dobkin BH used the hemiplegic rehabilitation theory and upper limb physiological structure as the basis, combined with biological science, mechanical engineering, automatic control and other disciplines to design the upper limb functional rehabilitation robot. The control system of impedance control, and Simulink software was used to establish the simulation model of the control system, and the influence of the control parameters based on position impedance on the upper limb function control of rehabilitation robot was analyzed. The results of Dobkin B H show that the rehabilitation robot’s control effect on the upper limb function changes with the change of movement speed. The upper limb rehabilitation robot designed by Dobkin B H has good stability but its accuracy is lacking, and it needs to be improved [4][5]. Naranjo-Hernández David once proposed a new upper limb rehabilitation robot system based on virtual reality, which fully utilizes many advantages of robots participating in stroke upper limb rehabilitation. The system has the advantages of small size, light weight and rehabilitation interaction. Naranjo-Hernández David’s system is mainly composed of a haptic device called Phantom Premium, Upper Extremity Exoskeleton Rehabilitation Device (ULERD) and virtual reality environment. It has been experimentally proved that Naranjo-Hernández David’s method is accurate and convenient during the rehabilitation process However, the economy is not strong and needs to be strengthened [6][7].

This article adopts the Internet of Things remote control technology and designs the upper limb rehabilitation robot system. In this paper, the relevant theory of the remote control of the Internet of Things is first elaborated, then from the perspective of human kinematics, the motion model of the upper limb rehabilitation robot is constructed, and finally, the upper limb rehabilitation robot system based on the Internet of Things remote control is designed and set The corresponding experiment was carried out to test the system. The test results show that the system in this paper has good accuracy and dynamic performance.SECTION II.

Internet of Things Remote Control

The so-called remote control technology refers to the technology that the Internet controls and manages remote devices to control and manage signals based on signals. Its software usually includes client-side and server-side programs. As the Internet of Things becomes more and more popular, remote control technology is also popularized. It can achieve the effect of unconventional remote control through IoT media [8][9].

A. Internet of Things

The Internet of Things realizes the mutual exchange, mutual knowledge, and interactive information exchange between “machines and machines”. It can also be understood that through a variety of communication technologies, the Internet of Things is a very complex and diverse system technology.. According to the principles of information generation, transmission, processing and application, the Internet of Things can be divided into four levels: perception recognition layer, network construction layer, management service layer and integrated application layer [10][11].

1) Perception Recognition Layer

What is the core technology of the Internet of Things? It is perception and recognition, so the perception recognition layer is very important for the Internet of Things. So let’s take a look at what the perceptual recognition layer includes. The level of perceptual recognition includes radio frequency identification, wireless sensors and automatic information production equipment. Not only that, but also includes a variety of intelligent information used to artificially produce electronic products. It can be said that as an emerging technology, wireless sensor networks mainly use different types of sensors to obtain large-scale, long-term, real-time information on environmental status and behavior patterns [12].

2) Network Building Layer

The main function of this layer is to connect lower-level data (perceived recognition-level data) to higher levels such as the Internet for its use. The Internet and next-generation Internet (including IPv6 and other technologies) are the core networks of the Internet of Things. Various wireless networks on the edge can provide network access services anytime and anywhere. The existing WIMAX technology is included in the scope of the wireless metropolitan area network, and its role is to provide high-speed data transmission services in the metro area (about 100 km). On the other hand, the wireless local area network also includes the WIFI that almost every household is currently trying. The use of WIFI is very wide. The main function is to provide network access services for users in a certain area (family, campus, restaurant, airport, etc.). Not only that, the wireless personal area network also includes Bluetooth, ZigBee and other communication protocols. These several things have a common feature, that is, low power consumption, low transmission rate, short distance, generally used for personal electronic product interconnection, industrial equipment Control and other fields. The various types of wireless networks listed above are suitable for different environments and work together to provide convenient network access so that the Internet of Things can be achieved [13].

3) Management Service Layer

By supporting high-performance computer technology and large-capacity storage, the management service level can efficiently and reliably organize large-scale data and provide an intelligent support platform for high-level industry applications. Storage is the first step in information processing. The database system and various mass storage technologies developed later, even including network storage (such as data centers), have now been widely used in information technology, finance, telecommunications, automation, etc. These industries. Faced with massive amounts of information, how to organize and search for effective data is a key issue. Therefore, the main feature of the management service layer is “wisdom”. Through rich and detailed data, mechanical learning, data mining, expert systems and other means, it serves the management ’s The function is increasingly powerful [14].

4) Comprehensive Application Layer

What was the original role of the Internet? It is used to achieve computer-to-computer communication, and then developed into a connection between users and people as the main body, and the times are changing. Now, it is moving towards the goal of connecting things-things-people. Not only that, along with this process, network applications have also undergone tremendous changes, from the initial transmission of files and emails with basic functions of data services to user-centric applications. In addition, the layers of the Internet of Things are relatively independent but closely connected. Below the integrated application layer, different technologies at the same layer are complementary and suitable for different environments, forming a complete set of response strategies for this level of technology, and at different levels, providing different technical compositions and combinations to Create a complete solution according to the requirements of the implementation [15].

The network topology diagrams of the mobile communication network and the wireless sensor network are shown in Figure 1 and Figure 2, respectively.

FIGURE 1. - Mobile communication network topology.


Mobile communication network topology.

View All

FIGURE 2. - Wireless sensor network topology.


Wireless sensor network topology.

View All

From Figure 1 and Figure 2 we can see the network topology of the mobile communication network and wireless sensor network. The sensor is the first basic link to realize the automatic monitoring function of the system.It is generally composed of sensitive components, conversion originals, conversion circuits and auxiliary power sources.It can convert the sensed information into electrical signals or other output forms according to certain rules. So as to transmit and process information [16].

Continue —–>

, , , , , , , , , , , , ,

Leave a comment

[Abstract + References] Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton


Stroke has become the second most disabling disease in the world. Due to the intensive demand for physical therapists and the severe dependence on hospitals, the cost for the treatment of stroke patients is huge. As the most flexible limb of the human body, the hand faces more severe challenges, which has a much lower degree of recovery than the upper and lower limbs. In the face of these challenges, a new treatment, exoskeleton-based rehabilitation, has demonstrated new vitality. This paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers, which would greatly improve the motor ability of stroke patients. Through the control of the six drive motors, the exoskeleton could achieve most of the hand’s freedom of training. At the same time, we propose a multi-modal intent recognition method based on machine vision and machine speech. Under specific rehabilitation training scenarios, both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton, overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.


1. M. P. Lindsay, B. Norrving, R. L. Sacco, M. Brainin, W. Hacke, S. Martins, et al., “World stroke organization (wso): Global stroke fact sheet 2019”, 2019.CrossRef  Google Scholar 

2. [online] Available: Show Context

3. K. B. Lee, S. H. Lim, K. H. Kim, K. J. Kim, Y. R. Kim, W. N. Chang, et al., “Six-month functional recovery of stroke patients: a multi-time-point study”, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation, vol. 38, no. 2, pp. 173, 2015. Show Context CrossRef  Google Scholar 

4. P. Polygerinos, S. Lyne, Z. Wang, L. F. Nicolini, B. Mosadegh, G. M. Whitesides, et al., “Towards a soft pneumatic glove for hand rehabilitation”, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1512-1517, 2013. Show Context View Article Full Text: PDF (1334KB) Google Scholar 

5. J. Yi, X. Chen and Z. Wang, “A three-dimensional- printed soft robotic glove with enhanced ergonomics and force capability”, IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 242-248, 2017. Show Context View Article Full Text: PDF (676KB) Google Scholar 

6. N. Ho, K. Tong, X. Hu, K. Fung, X. Wei, W. Rong, et al., “An emg-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation”, 2011 IEEE international conference on rehabilitation robotics, pp. 1-5, 2011. Show Context View Article Full Text: PDF (848KB) Google Scholar 

7. S. Park, L. Weber, L. Bishop, J. Stein and M. Ciocarlie, “Design and development of effective transmission mechanisms on a tendon driven hand orthosis for stroke patients”, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2281-2287, 2018. Show Context View Article Full Text: PDF (2666KB) Google Scholar 

8. L. Gerez and M. Liarokapis, “An underactuated tendon-driven wearable exo-glove with a four-output differential mechanism”, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6224-6228, 2019. Show Context View Article Full Text: PDF (3125KB) Google Scholar 

9. T. Bützer, J. Dittli, J. Lieber, H. J. van Hedel, A. Meyer-Heim, O. Lambercy, et al., “Pexo- a pediatric whole hand exoskeleton for grasping assistance in task-oriented training”, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 108-114, 2019. Show Context View Article Full Text: PDF (1216KB) Google Scholar 

10. M. Mirakhorlo, N. Van Beek, M. Wesseling, H. Maas, H. Veeger and I. Jonkers, “A musculoskeletal model of the hand and wrist: model definition and evaluation”, Computer methods in biomechanics and biomedical engineering, vol. 21, no. 9, pp. 548-557, 2018. Show Context CrossRef  Google Scholar 

11. M. Suarez-Escobar and E. Rendon-Velez, “An overview of robotic/mechanical devices for post-stroke thumb rehabilitation”, Disability and Rehabilitation: Assistive Technology, vol. 13, no. 7, pp. 683-703, 2018. Show Context CrossRef  Google Scholar 

12. M. Li, Z. Liang, B. He, C.-G. Zhao, W. Yao, G. Xu, et al., “Attention-controlled assistive wrist rehabilitation using a low-cost eeg sensor”, IEEE Sensors Journal, vol. 19, no. 15, pp. 6497-6507, 2019. Show Context View Article Full Text: PDF (4401KB) Google Scholar 

13. D. Huggins-Daines, M. Kumar, A. Chan, A. W. Black, M. Ravishankar and A. I. Rudnicky, “Pocketsphinx: A free real-time continuous speech recognition system for hand-held devices”, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, pp. I-I, 2006. Show Context View Article Full Text: PDF (89KB) Google Scholar 

14. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified real-time object detection”, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. Show Context View Article Full Text: PDF (1742KB) Google Scholar 


, , , , , , , , , , , , , ,

Leave a comment

[Abstract + References] Toward Human-Powered Lower Limb Exoskeletons: A Review – Conference paper


Most of the commercially available exoskeletons use rechargeable Li-ion batteries, which require frequent charging. The battery charging becomes a big bottleneck, when the person, wearing the exoskeleton, needs to go for a week trip on trekking or mountaineering. In order to make batteries more reliable and portable, an alternative energy source can be a good option. Human-powered devices are useful as an emergency electric power source, during natural disaster, war, or civil disturbance make regular power supplies unavailable. These devices have also been treated as an economical and environment-friendly option for use in underdeveloped countries, where batteries may be expensive and main power supply is unreliable or sometimes unavailable. Some of the environmental-energy-producing sources are piezoelectric devices, vibrational sources, RF transmitters, etc., where each method produces different amount of electricity. Some of these sources do not produce enough energy to charge an exoskeleton’s battery. Therefore, in this article, an effort has been made to review the human-powered products in order to develop a mechanism that can be used for charging the battery of exoskeletons. Human power is defined as the use of human work for energy generation. The energy is harvested from the user’s daily actions (walking, breathing, body heat, blood pressure, finger motion, etc.). This paper compares the various conventional and alternative methods to charge lower limb exoskeletons to be used for elderly people.


  1. 1.Kazerooni, H., Steger, R.: The berkeley lower extremity exoskeleton. Trans. ASME, J. Dyn. Syst. Meas. Control. 128, 14–25 (2006)CrossRefGoogle Scholar
  2. 2.Huang, G.T.: Wearable robots, Technol. Rev., pp. 70–73, (2004)Google Scholar
  3. 3.Kawamoto, H., Lee, S., Kanbe, S., Sankai, Y.: Power assist method for HAL-3 using EMG-based feedback controller. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1648–1653, (2003)Google Scholar
  4. 4.Yamamoto, K, Hyodo, H., Ishii, M., Matsuo, T.: Development of power assisting suit for assisting nurse labor. JSME Int. J., Ser. C., 3. 45, 703–711 (2002)Google Scholar
  5. 5.Pratt J.E., Krupp, B.T., Morse, J.C., Collins, S.H.: The RoboKnee: an exoskeleton for enhancing strength and endurance during walking. In: Proceeding International Conference on Robotics and Automation New Orleans, LA, pp. 2430–2435, (2004)Google Scholar
  6. 6.Honda: The power of dreams,
  7. 7.Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. On Robotics, 1. 24 (2008)Google Scholar
  8. 8.Brokaw, E.B., Black, I., Holley, R.J., Lum, P.S.: Hand spring operated movement enhancer (handsome): a portable, passive hand exoskeleton for stroke rehabilitation. IEEE Tran. on Neu. Sys. and Rehab. Engg., 4. 19, 391–399 (2011)Google Scholar
  9. 9.Toyama, S., Yamamoto, G.: Development of wearable-agri-robot: mechanism for agricultural work. In: IEEE International Conference on Intelligent Robotics and Systems, pp. 5801–5806, (2009)Google Scholar
  10. 10.Shima, K., Eguchi, R., Shiba, K., Tsuji, J.: CHRIS: cybernetic human-robot interface systems. In: Proceedings of International Symposium on Robotics, vol. 36 (2005)Google Scholar
  11. 11.Egawa, S., Takeuchi, I., Koseki, A., Ishii, T.: Electrically assisted walker with supporter-embedded force-sensing device. In: 8th International Conference on Rehabilitation Robotics, (2003)Google Scholar
  12. 12.Toyota motor corporation,
  13. 13.Colson, C.M., Nehrir, M.H.: Evaluating the benefits of a hybrid solid oxide fuel cell combined heat and power plant for energy sustainability and emissions avoidance. IEEE Trans. Energy Convers. 1(26), 140–148 (2011)CrossRefGoogle Scholar
  14. 14.Lee, J., Koo, D., Moon, S., Han, C.: Design of an axial flux permanent magnet generator for a portable hand crank generating system. IEEE Tran. on Magn., 11. 48, (2012)Google Scholar
  15. 15.Rao, Y., Cheng, S., Arnold, D. P.: An energy harvesting system for passively generating power from human activities. J. Micromech. Microengg. 11. 23, (2013)Google Scholar
  16. 16.Huong, H.O.C., Sarah, S., Parasuraman, S., Ahamed-Khan, MKA., Elamvazuthi, I.: Energy harvesting from human locomotion: gait analysis, design and state of art. In: Int. Con. on Robot PRIDE Proc. Comp. Sci. Vol. 42, pp. 327 – 335, (2014)Google Scholar
  17. 17.Bowers, B.J., Arnold, D.P.: Spherical, rolling magnet generators for passive energy harvesting from human motion. J. Micromech. Microengg. 9. Vol. 19, (2009)Google Scholar
  18. 18.Roundy, S.J.: Energy scavenging for wireless sensor nodes with a focus on vibration to electricity conversion. Diss. University of California, Berkeley (2003)Google Scholar
  19. 19.Sterken, T., Fiorini, P., Baert, K., Borghs, G., Puers, R.: Novel design and fabrication of a MEMS electrostatic vibration scavenger. In: Power MEMS Conference, pp. 18–21, (2004)Google Scholar
  20. 20.Ani, S.O., Bang, D., Polinder, H., Lee, J.Y., Moon, S.R., Koo, D.H.: Human powered axial flux permanent magnet machines: review and comparison. In: IEEE In Energy Conversion Congress and Exposition (ECCE), pp. 4165–4170, (2010)Google Scholar
  21. 21.Ani, S.O., Bang, D., Polinder, H., Lee, J.Y., Moon, S.R., Koo, D.H.: Design of portable axial flux permanent magnet machines for human power generation. In: IEEE International Conference on Electrical Machines and Systems (ICEMS), pp. 414–417, 2010Google Scholar
  22. 22.Louie, H., Peng, K., Hoffstetter, E., Szablya, S.J.: Design and testing of a small human-powered generator for developing rural communities. In: IEEE North American Power Symposium (NAPS), pp. 1–8, (2010)Google Scholar
  23. 23.Snyder, D.S.: Vibrating transducer power supply for use in abnormal tire condition warning systems. U.S. Patent 4, 384,482 (1983)Google Scholar
  24. 24.Snyder, D.S.: Piezoelectric reed power supply for use in abnormal tire condition warning systems. U.S. Patent 4, 510,484, (1985)Google Scholar
  25. 25.Sodano, H.A., Park, G., Leo, D.J., Inman, D.J.: Use of piezoelectric energy harvesting devices for charging batteries. Smart Struct. Mater.: Smart Sensor Technol. Meas. Syst. Proc. SPIE. 5050, 101–108 (2003)Google Scholar
  26. 26.Roundy, S., Wright, P.: A piezoelectric vibration based generator for wireless electronics. Smart Mater. Struct. 13, 1131–1142 (2004)CrossRefGoogle Scholar
  27. 27.Torres, E.O., Rincon-Mora, G.A.: Electrostatic energy-harvesting and battery-charging cmos system prototype. IEEE Trans. on Circuits and Syst. 9(56):1938–1948 (2008)Google Scholar
  28. 28.Beeby, S.P., Tudor, M. J., White, N. (2006) Energy harvesting vibration sources for microsystems applications. Meas. Sci. and Tech. 12(17):175–195Google Scholar
  29. 29.Vullers, R.J.M., Schaijk, R.V., Doms, I., Hoof, C.V., Mertens, R.: Micropower energy harvesting. Solid-State Electron. 53, 684–693 (2009)CrossRefGoogle Scholar
  30. 30.Neerg trading LTD. catalogue human power generators,
  31. 31.Tiwari, P.S., Gite, L.P., Pandey, M.M., Shrivastava, A.K.: Pedal power for occupational activities: effect of power output and pedaling rate on physiological responses. Int. J. Ind. Ergon. 3(41), 261–267 (2011)CrossRefGoogle Scholar
  32. 32.Strzelecki, R., Jarnut, M., Benysek, G.: Exercise bike powered electric generator for fitness club appliances. In: European Conference on Power Electronics and Applications, pp. 1–8, (2007)Google Scholar
  33. 33.Mechtenberg, A.R., Borchers, K., Miyingo, E.W., Hormasji, F., Hariharan, A., Makanda, J.V., Musaazi, M.K.: Human power (HP) as a viable electricity portfolio option below 20 W/Capita. Energy Sust. Dev. 16, 125–145 (2012)CrossRefGoogle Scholar
  34. 34.Bang, D., Ani, S., Polinder, H., Lee, J., Moon, S., Koo, D.: Design of portable axial flux permanent magnet machines for human power generation. IEEE Trans. on Magn., 11. vol. 48, pp. 2977–2980, (2012)Google Scholar
  35. 35.Ashe, S., Navarro, S.,: Merry-Go-Round Human Powered Generators. Senior Project, California Polytechnic State University, (2013–14)Google Scholar
  36. 36.Bock, T., Linner, T., Ikeda, W.: Exoskeletons and humanoid robotic technology in construction and built environment, INTECH open Access Publisher, (2012)Google Scholar
  37. 37.Tariq, M., Shamsi, K., Akhtar, T.: A portable manual charkha based power generation system for rural areas. ISESCO, J. Sci. Technol. 16(9), 89–93 (2013)Google Scholar
  38. 38.Linqiang, L., Dahu, W., Tong, Z., Mingke, H.: A manual mobile phone charger. In: International Conference on Electrical and Control Engineering, pp. 79–82, (2010)Google Scholar
  39. 39.Windstream Power LLC—Permanent Magnet DC generators for wind and pedal power,
  40. 40.Jansen, A., Slob, P.: Human power: comfortable one-hand cranking. Presented at International conference on engineering design (ICED), Stockholm, August (2003)Google Scholar
  41. 41.Moyers, W.L., Coombe, H.S., Hartman, A.: Harvesting energy with hand-crank generators to support dismounted soldier missions.
  42. 42.Lopez, E.P.: Design and testing of a novel human-powered generator device as a backup solution to power Cranfield’s nano-membrane toilet. M.Sc. thesis, Cranfield University, (2014)Google Scholar
  43. 43.Foot Powered Generator,
  44. 44.Riener, R., Lünenburger, L., Maier, I.C., Colombo, G., Dietz, V.: Locomotor training in subjects with sensorimotor deficits: an overview of the robotic gait orthosis lokomat. J. Healthc Eng. 1(2), 197–216 (2010)Google Scholar


, , , , , ,

Leave a comment

[ARTICLE] Evaluation of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after Stroke trial: descriptive analysis of intervention fidelity, goal selection and goal achievement – Full Text



To report the fidelity of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after stroke (RATULS) randomized controlled trial, the types of goals selected and the proportion of goals achieved.


Descriptive analysis of data on fidelity, goal selection and achievement from an intervention group within a randomized controlled trial.


Out-patient stroke rehabilitation within four UK NHS centres.


259 participants with moderate-severe upper limb activity limitation (Action Research Arm Test 0–39) between one week and five years post first stroke.


The enhanced upper limb therapy programme aimed to provide 36 one-hour sessions, including 45 minutes of face-to-face therapy focusing on personal goals, over 12 weeks.


7877/9324 (84%) sessions were attended; a median of 34 [IQR 29–36] per participant. A median of 127 [IQR 70–190] repetitions were achieved per participant per session attended. Based upon the Canadian Occupational Performance Measure, goal categories were: self-care 1449/2664 (54%); productivity 374/2664 (14%); leisure 180/2664 (7%) and ‘other’ 661/2664 (25%). For the 2051/2664 goals for which data were available, 1287 (51%) were achieved, ranging between 27% by participants more than 12 months post stroke with baseline Action Research Arm Test scores 0–7, and 88% by those less than three months after stroke with scores 8–19.


Intervention fidelity was high. Goals relating to self-care were most commonly selected. The proportion of goals achieved varied, depending on time post stroke and baseline arm activity limitation.


Up to 80% of stroke survivors have difficulties using their affected arm in daily activities,1 which often persist in the longer term, impacting on the ability to engage social roles and on autonomy.2 There is a need for further high quality evidence to support interventions to improve arm function after stroke.1,3,4 Repetitive functional task training has shown promise for improving arm function,3,5 and therefore further trials of this type of intervention are particularly important. The Robot-Assisted Training for the Upper Limb after Stroke (RATULS) randomized controlled trial, the largest of its kind to date (n = 770), was published recently.6 Participants were randomized to receive robot-assisted training, an enhanced upper limb therapy programme (where repetitive functional task practice focused on personal goals), or usual care.6 There was little evidence of a difference in the primary outcome of arm activity limitation (i.e. success in attaining pre-specified improvement in the Action Research Arm Test7,8 score at three months) between randomization groups. However, participants who were randomized to receive the enhanced upper limb therapy programme performed significantly better in a number of secondary outcomes when compared to those who received usual care. Clinically important benefits at the end of the three month intervention period were observed in measures of impairment (Fugl-Meyer Assessment Motor Score),8,9 activities of daily living and mobility (Stroke Impact Scale).10 Additionally, there were statistically significant improvements which were not considered clinically important, as the confidence intervals did not include values that are currently deemed to be Minimum Clinically Important Differences. These statistically significant improvements were in measures of arm function (Action Research Arm Test), hand function (Stroke Impact Scale),10 and activities of daily living (Barthel Activity of Daily Living Index)11 – with the latter continuing to 6 months follow-up. Participants randomized to receive the enhanced upper limb therapy programme also performed significantly better than those randomized to receive robot-assisted training in measures of activities of daily living at three months (Stroke Impact Scale10 and Barthel Index11) but these improvements also did not reach the threshold for being considered clinically important.6

It is important that the development and fidelity of interventions are fully reported to enable the results of a trial to be interpreted, and for the intervention to be replicable in routine clinical practice or future research. However, stroke rehabilitation trials often fall short in terms of reporting these aspects.12,13 The development and description of the enhanced upper limb therapy programme followed the Template for Intervention Description and Replication (TIDieR) framework,12 and the planned delivery of the intervention (TIDieR items 1–11) has been reported.14 The aim of this paper is to report the intervention fidelity (TIDieR item 12) and a descriptive analysis of the types of personal goals selected and the proportion achieved.[…]

Continue —->

, , , , , , , , ,

Leave a comment

[ARTICLE] Exoskeleton use in post-stroke gait rehabilitation: a qualitative study of the perspectives of persons post-stroke and physiotherapists – Full Text



Wearable powered exoskeletons are a new and emerging technology developed to provide sensory-guided motorized lower limb assistance enabling intensive task specific locomotor training utilizing typical lower limb movement patterns for persons with gait impairments. To ensure that devices meet end-user needs it is important to understand and incorporate end-users perspectives, however research in this area is extremely limited in the post-stroke population. The purpose of this study was to explore in-depth, end-users perspectives, persons with stroke and physiotherapists, following a single-use session with a H2 exoskeleton.


We used a qualitative interpretive description approach utilizing semi-structured face to face interviews, with persons post-stroke and physiotherapists, following a 1.5 h session with a H2 exoskeleton.


Five persons post-stroke and 6 physiotherapists volunteered to participate in the study. Both participant groups provided insightful comments on their experience with the exoskeleton. Four themes were developed from the persons with stroke participant data: (1) Adopting technology; (2) Device concerns; (3) Developing walking ability; and, (4) Integrating exoskeleton use. Five themes were developed from the physiotherapist participant data: (1) Developer-user collaboration; (2) Device specific concerns; (3) Device programming; (4) Patient characteristics requiring consideration; and, (5) Indications for use.


This study provides an interpretive understanding of end-users perspectives, persons with stroke and neurological physiotherapists, following a single-use experience with a H2 exoskeleton. The findings from both stakeholder groups overlap such that four over-arching concepts were identified including: (i) Stakeholder participation; (ii) Augmentation vs. autonomous robot; (iii) Exoskeleton usability; and (iv) Device specific concerns. The end users provided valuable perspectives on the use and design of the H2 exoskeleton, identifying needs specific to post-stroke gait rehabilitation, the need for a robust evidence base, whilst also highlighting that there is significant interest in this technology throughout the continuum of stroke rehabilitation.


Over the period 1990–2017 there has been a 3% increase in age-standardized rates of global stroke prevalence [1] and a 33% decrease in mortality due to improved risk factor control and treatments [2]. Therefore, stroke survivors are living longer with mild to severe lifelong disabilities requiring long term assistance [1]. As a result, stroke presents a significant socioeconomic burden accounting for the largest proportion of total disability adjusted life years (47.3%) of neurological disorders [3]. Walking impairments, one aspect of stroke disabilities, negatively impact independence and quality of life [4], and recovery of walking is a primary goal post-stroke [5].

Wearable powered exoskeletons are a new and emerging technology originally developed as robots to enable persons who were completely paralyzed due to spinal cord injury to stand and walk [67], but more recently developed to provide sensory-guided motorized lower limb assistance to persons with gait impairments [8]. They require the active participation of the user from the perspective of integrating postural control/balance and the locomotion pattern in real life environments whilst simultaneously providing assistance to achieve typical lower limb movement patterns in a task specific manner [8]. The Exo-H2 is a novel powered exoskeleton in that it has six actuated joints, the hip, knee and ankle bilaterally, and uses an assistive gait control algorithm to provide lower limb assistance when the gait pattern deviates from a prescribed pattern [9]. As stroke impairments typically influence hip, knee and ankle movements the H2 was considered an appropriate exoskeleton for our study [810].

Significant limitations persist in current exoskeleton designs such as weight, cost, size, speed and efficiency [11]. Although end-users’ perspectives are essential in the design and development of assistive technology [1213], there is a paucity of literature from both persons with disabilities and physiotherapists (PTs) perspectives [1415]. Over the last decade end-user perspectives have primarily been studied in spinal cord injury (SCI) in which four studies used semi-structured interviews [16,17,18,19], and 3 studies used survey methods [20,21,22] with sample size ranging from 3 to 20 persons. However, these studies included both complete and incomplete SCI with most participants being non-ambulatory representing a very different end-user population compared to persons post-stroke. A further two studies reported end-user perspectives using survey methods with persons with multiple sclerosis (MS) [23], and persons with MS, SCI or acquired brain injury (ABI) [24]. Wolff et al.,(2014) utilized an online survey to evaluate perspectives on potential use of exoskeletons with wheelchair users, primarily persons with SCI, and healthcare professionals, but no PTs were included [25]. To date only one study by Read et al.,(2020) specifically investigated perspectives of 3 PTs on exoskeleton use using semi-structured interviews with persons with SCI or stroke. Currently, a mixed-methods study is underway to investigate perspectives of PTs and persons with stroke [26]. Thus, further research is needed to explore in-depth, utilizing a qualitative research approach, end-users’ perspectives on lower limb exoskeleton use in post-stroke gait rehabilitation.

It is important to understand and incorporate end-user perspectives [27], persons post-stroke and physiotherapists, with respect to the design of exoskeletons and their implementation to effectively facilitate uptake both in clinical practice and community settings. Therefore, the purpose of our study is to explore the perspectives of persons post-stroke and physiotherapists following a 1.5 h single-use session with a H2 exoskeleton.[…]

Continue —->

, , , , , , ,

Leave a comment

[Abstract + References] Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons in Slope Scenarios


Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.


1. A. Esquenazi, M. Talaty, A. Packel and M. Saulino, “The rewalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury”, Am J Phys Med Rehabil, vol. 91, no. 11, pp. 911-921, 2012. Show Context CrossRef  Google Scholar 

2. L. E. Miller, A. K. Zimmermann and W. G. Herbert, “Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: systematic review with meta-analysis”, Medical Devices, vol. 9, pp. 455, 2016. Show Context CrossRef  Google Scholar 

3. C. Tefertiller, K. Hays, J. Jones, A. Jayaraman, C. Hartigan, T. Bush-nik, et al., “Initial outcomes from a multicenter study utilizing the indego powered exoskeleton in spinal cord injury”, Topics in Spinal Cord Injury Rehabilitation, 2017. Show Context Google Scholar 

4. H. Kawamoto, S. Lee, S. Kanbe and Y. Sankai, “Power assist method for hal-3 using emg-based feedback controller”, IEEE International Conference on Systems Man and Cybernetics, vol. 2, pp. 1648-1653, 2003. Show Context View Article Full Text: PDF (459KB) Google Scholar 

5. S. Kajita, F. Kanehiro, K. Kaneko and K. Yokoi, “The 3d linear inverted pendulum mode: a simple modeling for a biped walking pattern generation”, IEEE/RSJ International Conference on Intelligent Robots and Systems 2001. Proceedings, vol. 1, pp. 239-246, 2001. Show Context View Article Full Text: PDF (558KB) Google Scholar 

6. S. Kajita and K. Tani, “Study of dynamic walk control of a biped robot on rugged terrain using the linear inverted pendulum mode”, Transactions of the Society of Instrument & Control Engineers, vol. 27, no. 2, pp. 177-184, 2009. Show Context CrossRef  Google Scholar 

7. S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Harada, K. Yokoi, et al., “Biped walking pattern generation by using preview control of zero-moment point”, IEEE International Conference on Robotics and Automation 2003. Procedings. ICRA, pp. 1620-1626, 2003. Show Context View Article Full Text: PDF (382KB) Google Scholar 

8. M. Morisawa, S. Kajita, F. Kanehiro and K. Kaneko, “Balance control based on capture point error compensation for biped walking on uneven terrain”, IEEE-RAS International Conference on Humanoid Robots, pp. 734-740, 2012. Show Context View Article Full Text: PDF (1560KB) Google Scholar 

9. S. Kajita, H. Hirukawa, K. Harada and K. Yokoi, Introduction to humanoid robotics, Springer Berlin Heidelberg, 2014. Show Context Google Scholar 

10. W. Pierre-Brice, “Model predictive control for biped walking motion generation”, Journal of the Robotics Society of Japan, vol. 32, no. 6, pp. 503-507, 2014. Show Context Google Scholar 

11. P. B. Wieber and C. Chevallereau, “Online adaptation of reference trajectories for the control of walking systems”, Robotics & Autonomous Systems, vol. 54, no. 7, pp. 559-566, 2006. Show Context CrossRef  Google Scholar 

12. H. Diedam, D. Dimitrov, P. B. Wieber and K. Mombaur, “Online walking gait generation with adaptive foot positioning through linear model predictive control”, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1121-1126, 2008. Show Context Google Scholar 

13. C. Brasseur, A. Sherikov, C. Collette, D. Dimitrov and P.-B. Wieber, “A robust linear mpc approach to online generation of 3d biped walking motion”, Humanoid Robots (Humanoids) 2015 IEEE-RAS 15th International Conference on, pp. 595-601, 2015. Show Context View Article Full Text: PDF (255KB) Google Scholar 

14. J. Englsberger and C. Ott, “Integration of vertical com motion and angular momentum in an extended capture point tracking controller for bipedal walking”, IEEE-RAS International Conference on Humanoid Robots, pp. 183-189, 2012. Show Context View Article Full Text: PDF (252KB) Google Scholar 

15. M. Krause, J. Englsberger, P. B. Wieber and C. Ott, “Stabilization of the capture point dynamics for bipedal walking based on model predictive control”, IFAC Proceedings Volumes, vol. 45, no. 22, pp. 165-171, 2012. Show Context CrossRef  Google Scholar 

16. J. Englsberger, C. Ott and A. Albu-Schaffer, “Three-dimensional bipedal walking control using divergent component of motion”, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2600-2607, 2013. Show Context View Article Full Text: PDF (434KB) Google Scholar 

17. J. Englsberger, T. Koolen, S. Bertrand and J. Pratt, “Trajectory generation for continuous leg forces during double support and heel-to-toe shift based on divergent component of motion”, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4022-4029, 2014. Show Context View Article Full Text: PDF (412KB) Google Scholar 

18. S. A. Murray, K. H. Ha, C. Hartigan and M. Goldfarb, “An assistive control approach for a lower-limb exoskeleton to facilitate recovery of walking following stroke”, IEEE Transactions on Neural Systems & Rehabilitation Engineering, vol. 23, no. 3, pp. 441-449, 2015. Show Context View Article Full Text: PDF (1319KB) Google Scholar 

19. B. E. Lawson, H. A. Varol, A. Huff, E. Erdemir and M. Goldfarb, “Control of stair ascent and descent with a powered transfemoral prosthesis”, IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, vol. 21, no. 3, pp. 466-473, 2013. Show Context View Article Full Text: PDF (1330KB) Google Scholar 

20. A. H. Shultz, B. E. Lawson and M. Goldfarb, “Variable cadence walking and ground adaptive standing with a powered ankle prosthesis”, IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, vol. 24, no. 4, pp. 495, 2015. Show Context View Article Full Text: PDF (1260KB) Google Scholar 

21. T. Koolen, T. D. Boer, J. Rebula, A. Goswami and J. Pratt, “Capturability-based analysis and control of legged locomotion part 1: Theory and application to three simple gait models”, International Journal of Robotics Research, vol. 31, no. 9, pp. 1094-1113, 2012. Show Context CrossRef  Google Scholar 

22. J. Pratt, J. Carff, S. Drakunov and A. Goswami, “Capture point: A step toward humanoid push recovery”, IEEE-RAS International Conference on Humanoid Robots, pp. 200-207, 2006. Show Context View Article Full Text: PDF (798KB) Google Scholar 

23. J. E. Pratt, P. D. Neuhaus, M. Johnson, J. Carff and B. T. Krupp, “Towards humanoid robots for operations in complex urban environments”, Proceedings of SPIE – The International Society for Optical Engineering, pp. 769-769, 2010. Show Context CrossRef  Google Scholar 

24. A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor and S. Schaal, “Dynamical movement primitives: Learning attractor models for motor behaviors”, Neural Computation, vol. 25, no. 2, pp. 328-373, 2013. Show Context View Article Full Text: PDF (2567KB) Google Scholar 

25. S. Schaal, Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics, Tokyo:Springer, 2006. Show Context CrossRef  Google Scholar 

26. P. Pastor, H. Hoffmann, T. Asfour and S. Schaal, “Learning and generalization of motor skills by learning from demonstration”, IEEE International Conference on Robotics and Automation, pp. 763-768, 2009. Show Context View Article Full Text: PDF (2900KB) Google Scholar 

27. R. Krug and D. Dimitrov, “Model predictive motion control based on generalized dynamical movement primitives”, Journal of Intelligent & Robotic Systems, vol. 77, no. 1, pp. 17-35, 2015. Show Context CrossRef  Google Scholar 

28. J. Nakanishi, J. Morimoto, G. Endo, G. Cheng, S. Schaal and M. Kawato, “A framework for learning biped locomotion with dynamical movement primitives”, IEEE/RAS International Conference on Humanoid Robots, pp. 925-940, 2005. Show Context Google Scholar 

29. S. Schaal and C. G. Atkeson, “Constructive incremental learning from only local information”, Neural Computation, vol. 10, no. 8, pp. 2047-2084, 1998. Show Context View Article Full Text: PDF (572KB) Google Scholar 


, , , , , , , , , , ,

Leave a comment

[ARTICLE] Evidence of Neuroplasticity: A Robotic Hand Exoskeleton Study for Post-Stroke Rehabilitation – Full Text PDF


Background: A novel electromechanical robotic-exoskeleton was designed in-house for rehabilitation of wrist joint and Metacarpophalangeal (MCP) joint.

Objective: The objective was to compare the rehabilitation effectiveness (clinical-scales and neurophysiological-measures) of robotic-therapy training-sessions with dose-matched control in patients with stroke.

Methods: An observational pilot study was designed with patients within 2 years of chronicity. Patients received an intervention of 20 sessions of 45-minutes each, five days a week for four-weeks) in Robotic-therapy Group (RG) (n=12) and conventional upper-limb rehabilitation in Control-Group (CG) (n=11). Clinical-scales– Modified Ashworth Scale, Active Range of Motion, Barthel-Index, Brunstrom-stage and Fugl-Meyer scale (Shoulder/Elbow and Wrist/Hand component), and neurophysiological-measures of cortical-excitability (using Transcranial Magnetic Stimulation) –Motor Evoked Potential and Resting Motor-threshold, were acquired pre and post-therapy.

Results: RG and CG showed significant improvement in all clinical motor-outcomes (p<0.05) except Modified Ashworth Scale in CG. RG showed significantly higher improvement over CG in Modified Ashworth Scale, Active Range of Motion and Fugl-Meyer (FM) scale and FM Wrist-/Hand component) (p<0.05). Increase in cortical-excitability in ipsilesional-hemisphere was found to be statistically significant in RG over CG, as indexed by decrease in Resting Motor-Threshold and increase in amplitude of Motor Evoked Potential (p<0.05). No significant changes were shown by the contralesional-hemisphere. Interhemispheric RMT-asymmetry evidenced significant changes in RG over CG (p<0.05) indicating increased cortical-excitability in ipsilesional-hemisphere along with interhemispheric changes.

Conclusion: Neurophysiological-changes in RG could be most likely a consequence of plastic-reorganization and use-dependent plasticity. Robotic-exoskeleton training could significantly improve motor-outcomes and cortical-excitability in patients with stroke.

1. Introduction

Stroke is one of the leading causes of mortality and morbidity worldwide (1). The ability to actively initiate extension movements at wrist and fingers against flexor-hypertonia is one of the key indicators of motor recovery (2),(3). Regaining hand-function and Activities of daily-living (ADL) is particularly impervious to therapy or rehabilitation pertaining to the complexity of motor-control needed for distal-joints (4). Conventional rehabilitation-therapy is time taking, labour-intensive and subjective, which with high clinical-load and absence of skilled resources gets difficult for the present medical and healthcare-system to provide appropriate or effective rehabilitation services (5).

Although rehabilitation with neuro-rehabilitation robots has shown encouraging clinical-results (5, 6, 15, 7–14), it is currently limited to a very few hospitals and not widely used because of associated high-cost and an infrastructural-requirement to station, size, complexity, set-up time, safety and usability restricting its success (16),(17),(18). Rehabilitation-strategies need to take into account the multifaceted nature of disability, which itself changes with time elapsed post-stroke and address with a multimodal-approach. Hence, the device needs to be flexible enough to accommodate a large patient-population. An effective rehabilitation device for hand should be able to facilitate a specific pattern of movements mirroring complex inter-joint coordination of hand with a patient-specific impairment, currently not integrated by the available devices.

In our previous work, we designed a robotic-hand exoskeleton for rehabilitation of the wrist and MCP (Metcarpo-phallengeal) joint, to synchronize wrist-extension with finger-flexion and wrist-flexion with finger-extension, mimicking ADL (19). With simple and easy-to-operate exoskeleton for low-resource settings, the exoskeleton targets spasticity through a synergy-based rehabilitation approach while also maintaining patient-initiated therapy through residual muscle-activity for maximizing voluntary effort. The lightweight and portable device has shown evidence of improvement in quantitative motor clinical-outcomes in patients with chronic stroke (19).

The aim of the present study was twofold. The first objective was to assess the clinical effectiveness of the novel robotic-exoskeleton device (19) and the second is comparison of its clinical-effectiveness with conventional upper-limb rehabilitation. We hypothesized that the exoskeleton could show higher improvement of distal-function and cortical-excitability in patients with stroke as compared to conventional-rehabilitation.[…]

Continue —->

Full Text PDF

Figure 2
Figure 2

, , , , , , , , , , , ,

Leave a comment

[Abstract] Development of a soft cable-driven hand exoskeleton for assisted rehabilitation training



Hand motor dysfunction has seriously reduced people’s quality of life. The purpose of this paper is to solve this problem; different soft exoskeleton robots have been developed because of their good application prospects in assistance. In this paper, a new soft hand exoskeleton is designed to help people conduct rehabilitation training.


The proposed soft exoskeleton is an under-actuated cable-driven mechanism, which optimizes the force transmission path and many local structures. Specifically, the path of force transmission is optimized and cables are wound around cam-shaped spools to prevent cables lose during fingers movement. Besides, a pre-tightening system is presented to adjust the preload force of the cable-tube. Moreover, a passive brake mechanism is proposed to prevent the cables from falling off the spools when the remote side is relaxed.


Finally, three control strategies are proposed to assist in rehabilitation training. Results show that the average correlation coefficient of trajectory tracking is 90.99% and this exoskeleton could provide steady clamping force up to 35 N, which could meet the demands of activities in daily living. Surface electromyography (sEMG)-based intention recognition method is presented to complete assistance and experiments are conducted to prove the effectiveness of the assisted grasping method by monitoring muscle activation, finger angle and interactive force.

Research limitations/implications

However, the system should be further optimized in terms of hardware and control to reduce delays. In addition, more clinical trials should be conducted to evaluate the effect of the proposed rehabilitation strategies.

Social implications

May improve the ability of hemiplegic patients to live independently.


A novel under-actuated soft hand exoskeleton structure is proposed, and an sEMG-based auxiliary grasping control strategy is presented to help hemiplegic patients conduct rehabilitation training.


, , , , , , , , , ,

Leave a comment

[Abstract] Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial



There is consistent evidence that robotic rehabilitation is at least as effective as conventional physiotherapy for upper extremity (UE) recovery after stroke, suggesting to focus research on which subgroups of patients may better respond to either intervention. In this study, we evaluated which baseline variables are associated with the response after the two approaches.


This is a secondary analysis of a randomized-controlled trial comparing robotic and conventional treatment for the UE. After the assigned intervention, changes of the Fugl-Meyer Assessment UE score by ≥ 5 points classified patients as responders to treatment. Variables associated with the response were identified in a univariate analysis. Then, variables independently associated with recovery were investigated, in the whole group, and the two groups separately.


A sample of 190 patients was evaluated after the treatment; 121 were responders. Age, baseline impairment, and neglect were significantly associated with worse response to the treatment. Age was the only independently associated variable (OR 0.967, p = 0.023). Considering separately the two interventions, age remained negatively associated with recovery (OR 0.948, p = 0.013) in the conventional group, while none of the variables previously identified were significantly associated with the response to treatment in the robotic group.


We found that, in our sample, age is significantly associated with the outcome after conventional but not robotic UE rehabilitation. Possible explanations may include an enhanced positive attitude of the older patients towards technological training and reduced age-associated fatigue provided by robotic-assisted exercise. The possibly higher challenge proposed by robotic training, unbiased by the negative stereotypes concerning very old patients’ expectations and chances to recover, may also explain our findings.

Download references


, , , , , , ,

Leave a comment

%d bloggers like this: