Posts Tagged Stroke patients

[Abstract] Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – Conference Paper

I. Introduction

Nowadays, a stroke is the fourth leading cause of death in the United States. In fact, every 40 seconds, someone in the US is having a stroke. Moreover, around 50% of stroke survivors suffer damage to the upper extremity [1]–[3]. Many actions of treating and recovering from a stroke have been developed over the years, but recent studies show that combining the recovery process with the existing rehabilitation plan provides better results and a raise in the patients quality of life [4]–[6]. Part of the stroke recovery process is a rehabilitation plan [7]. The process can be difficult, intensive and long depending on how adverse the stroke and which parts of the brain were damaged. These processes usually involve working with a team of health care providers in a full extensive rehabilitation plan, which includes hospital care and home exercises.


1. D. Tsoupikova, N. S. Stoykov, M. Corrigan, K. Thielbar, R. Vick, Y. Li, K. Triandafilou, F. Preuss, D. Kamper, “Virtual immersion for poststroke hand rehabilitation therapy”, Annals of biomedical engineering, vol. 43, no. 2, pp. 467-477, 2015.

2. J. E. Pompeu, T. H. Alonso, I. B. Masson, S. M. A. A. Pompeu, C. Torriani-Pasin, “The effects of virtual reality on stroke rehabilitation: a systematic review”, Motricidade, vol. 10, no. 4, pp. 111-122, 2014.

3. J.-H. Shin, S. B. Park, S. H. Jang, “Effects of game-based virtual reality on health-related quality of life in chronic stroke patients: A randomized controlled study”, Computers in biology and medicine, vol. 63, pp. 92-98, 2015.

4. R. W. Teasell, L. Kalra, “Whats new in stroke rehabilitation”, Stroke, vol. 35, no. 2, pp. 383-385, 2004.

5. E. McDade, S. Kittner, “Ischemic stroke in young adults” in Stroke Essentials for Primary Care, Springer, pp. 123-146, 2009.

6. P. Langhorne, J. Bernhardt, G. Kwakkel, “Stroke rehabilitation”, The Lancet, vol. 377, no. 9778, pp. 1693-1702, 2011.

7. C. J. Winstein, J. Stein, R. Arena, B. Bates, L. R. Cherney, S. C. Cramer, F. Deruyter, J. J. Eng, B. Fisher, R. L. Harvey et al., “Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the american heart association/american stroke association”, Stroke, vol. 47, no. 6, pp. e98-e169, 2016.

8. R. Ibanez, A. Soria, A. Teyseyre, M. Campo, “Easy gesture recognition for kinect”, Advances in Engineering Software, vol. 76, pp. 171-180, 2014.

9. R. Ibañez, A. Soria, A. R. Teyseyre, L. Berdun, M. R. Campo, “A comparative study of machine learning techniques for gesture recognition using kinect”, Handbook of Research on Human-Computer Interfaces Developments and Applications, pp. 1-22, 2016.

10. S. Bhattacharya, B. Czejdo, N. Perez, “Gesture classification with machine learning using kinect sensor data”, Emerging Applications of Information Technology (EAIT) 2012 Third International Conference on, pp. 348-351, 2012.

11. K. Laver, S. George, S. Thomas, J. E. Deutsch, M. Crotty, “Virtual reality for stroke rehabilitation”, Stroke, vol. 43, no. 2, pp. e20-e21, 2012.

12. G. Saposnik, M. Levin, S. O. R. C. S. W. Group et al., “Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians”, Stroke, vol. 42, no. 5, pp. 1380-1386, 2011.

13. K. R. Anderson, M. L. Woodbury, K. Phillips, L. V. Gauthier, “Virtual reality video games to promote movement recovery in stroke rehabilitation: a guide for clinicians”, Archives of physical medicine and rehabilitation, vol. 96, no. 5, pp. 973-976, 2015.

14. A. Estepa, S. S. Piriz, E. Albornoz, C. Martínez, “Development of a kinect-based exergaming system for motor rehabilitation in neurological disorders”, Journal of Physics: Conference Series, vol. 705, pp. 012060, 2016.

15. E. Chang, X. Zhao, S. C. Cramer et al., “Home-based hand rehabilitation after chronic stroke: Randomized controlled single-blind trial comparing the musicglove with a conventional exercise program”, Journal of rehabilitation research and development, vol. 53, no. 4, pp. 457, 2016.

16. L. Ebert, P. Flach, M. Thali, S. Ross, “Out of touch-a plugin for controlling osirix with gestures using the leap controller”, Journal of Forensic Radiology and Imaging, vol. 2, no. 3, pp. 126-128, 2014.

17. W.-J. Li, C.-Y. Hsieh, L.-F. Lin, W.-C. Chu, “Hand gesture recognition for post-stroke rehabilitation using leap motion”, Applied System Innovation (ICASI) 2017 International Conference on, pp. 386-388, 2017.

18. K. Vamsikrishna, D. P. Dogra, M. S. Desarkar, “Computer-vision-assisted palm rehabilitation with supervised learning”, IEEE Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 991-1001, 2016.

19. A. Butt, E. Rovini, C. Dolciotti, P. Bongioanni, G. De Petris, F. Cavallo, “Leap motion evaluation for assessment of upper limb motor skills in parkinson’s disease”, Rehabilitation Robotics (ICORR) 2017 International Conference on, pp. 116-121, 2017.

20. L. Di Tommaso, S. Aubry, J. Godard, H. Katranji, J. Pauchot, “A new human machine interface in neurosurgery: The leap motion (®). technical note regarding a new touchless interface”, Neuro-Chirurgie, vol. 62, no. 3, pp. 178-181, 2016.

21. O. Chapelle, “Training a support vector machine in the primal”, Neural computation, vol. 19, no. 5, pp. 1155-1178, 2007.

22. Y. Ma, G. Guo, Support vector machines applications, Springer, 2014.

23. J. Guna, G. Jakus, M. Pogačnik, S. Tomažič, J. Sodnik, “An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking”, Sensors, vol. 14, no. 2, pp. 3702-3720, 2014.

24. T. DOrazio, R. Marani, V. Renó, G. Cicirelli, “Recent trends in gesture recognition: how depth data has improved classical approaches”, Image and Vision Computing, vol. 52, pp. 56-72, 2016.

25. L. Motion, Leap motion sdk, 2015.


via Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – IEEE Conference Publication

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[Abstract] A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study  


The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.

Source: A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study – IEEE Xplore Document

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[ARTILE] Changes in gait kinematics and muscle activity in stroke patients wearing various arm slings – Full Text


Stroke patients often use various arm slings, but the effects of different slings on the joint kinematics and muscle activity of the arm in the gait have not been investigated. The effects of joint kinematics and muscle activity in the gait were investigated to provide suggestions for gait training for stroke patients. In all, 10 chronic stroke patients were voluntarily recruited. An eight-camera three-dimensional motion analysis system was used to measure joint kinematics while walking; simultaneously, electromyography data were collected for the anterior and posterior deltoids and latissimus dorsi. The amplitude of pelvic rotation on the less-affected side differed significantly among the different arm slings (P<0.05). Changes in the knee kinematics of the less-affected side also differed significantly (P<0.05), while there were no significant differences in the muscle activity of the affected arm. In stroke patients, an extended arm sling is more useful than no sling or a flexed arm sling in terms of the amplitude of the rotation of the less-affected pelvic side in the stance phase while walking. The less-affected knee joint is flexed more without a sling than with any sling. All arm slings support the extension of the contralateral knee.


Stroke is a major cause of morbidity worldwide. Approximately 800,000 patients have strokes annually (Lloyd-Jones et al., 2010). Patients with stroke have disabilities that result from paralysis, and most complain of difficulty walking (Jørgensen et al., 1995). Bovonsunthonchai et al. (2012) showed that the affected upper extremity is important for improving the performance and coordination of gait in stroke patients. In addition, the movement of the upper extremity improves the range of motion at the ankle as well as trunk stability (Stephenson et al., 2010).
Stroke patients often develop a subluxation of the shoulder on the affected side, because they can no longer support the weight of their own arm due to paralysis (Griffin et al., 1986). Consequently, arm slings are often necessary. Stroke patients often use a hemisling. Faghri et al. (1994) stated that use of a hemisling induced flexion synergy patterns of the upper trunk and delayed functional activity. However, few studies have examined how different arm slings, including a hemisling, affect the gait patterns of stroke patients. Reported studies have examined the hemisling in terms of the gait patterns (Yavuzer and Ergin, 2002), balance (Acar and Karatas, 2010), and energy consumption (Han et al., 2011) of stroke patients.
There are various types of arm sling, such as the flexed sling (a single-strap hemisling), extended sling (Bobath sling, Rolyan sling), GivMohr sling (Dieruf et al., 2005), and elastic arm sling (Hwang and An, 2015). The sling supports some of the weight of the arm and simultaneously limits the motion of the upper extremities. Pontzer et al. (2009)suggested that the arms serve as passive mass dampers to decrease the rotation of the torso and head. Lieberman et al. (20072008) also held that the arms serve as passive dampers to minimise vertical motion. The trunk and shoulders act as elastic linkages between the pelvis, shoulder girdle, and arms (Pontzer et al., 2009).
Some studies have examined the activities of the arm muscle during walking (Lieberman et al., 2007Prentice et al., 2001), while other studies have found that most of the arm swing is passive, while a small torque may actively occur in shoulder rotation (Jackson et al., 1978Kubo et al., 2004). The muscle activity of the upper extremities is still the subject of debate (Collins et al., 2009Kubo et al., 2004Kuhtz-Buschbeck and Jing, 2012). However, the restrictive effects and support provided by various arm slings could have different effects on the muscle activities of the affected arm in stroke patients.
Therefore, we investigated how the muscle activities of the affected arm and kinematic data taken during walking are influenced by flexion-type (hemisling), extension-type (Rolyan sling), and elastic arm slings under elastic tension. We discuss which arm should be used for clinical gait training.

Continue —> Changes in gait kinematics and muscle activity in stroke patients wearing various arm slings – ScienceCentral

Fig. 1 The conditions of the various arm slings: (A) none, (B) a flexed type, (C) an extended type, and (D) an elastic type.

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[ARTICLE] Effects of adjustment of transcranial direct current stimulation on motor function of the upper extremity in stroke patients – Full Text PDF


[Purpose] The purpose of this study was to examine the effects of transcranial direct current stimulation (tDCS) applied to the cerebral cortex motor area on the upper extremity functions of hemiplegic patients.

[Subjects and Methods] Twenty four Patients with hemiplegia resulting from a stroke were divided into two groups: a tDCS group that received tDCS and physical therapy and a control group that received only physical therapy. A functional evaluation of the two groups was performed, and an electrophysiological evaluation was conducted before and after the experiment. Statistical analyses were performed to verify differences before and after the experiment. All statistical significance levels were set at 0.05.

[Results] The results showed that functional evaluation scores for the elbow joint and hand increased after the treatment in both the experimental group and the control group, and the increases were statistically significantly different.

[Conclusion] tDCS was effective in improving the upper extremity motor function of stroke patients. Additional research is warranted on the usefulness of tDCS in the rehabilitation of stroke patients in the clinical field.

Source: Effects of adjustment of transcranial direct current stimulation on motor function of the upper extremity in stroke patients

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[Systematic Review] A Decade of Progress Using Virtual Reality for Poststroke Lower Extremity Rehabilitation: Systematic Review of the Intervention Methods – Full Text PDF

Objective. To develop a systematic review of the literature, to describe the different virtual reality (VR) interventions and interactive videogames applied to the lower extremity (LE) of stroke patients, and to analyse the results according to the most frequently used outcome measures.

Material and Methods. An electronic search of randomized trials between January 2004 and January 2014 in different databases (Medline, Cinahl, Web of Science, PEDro, and Cochrane) was carried out. Several terms (virtual reality, feedback, stroke, hemiplegia, brain injury, cerebrovascular accident, lower limb, leg, and gait) were combined, and finally 11 articles were included according to the established inclusion and exclusion criteria.

Results.The reviewed trials showed a high heterogeneity in terms of study design and assessment tools, which makes it difficult to compare and analyze the different types of interventions. However, most of them found a significant improvement on gait speed, balance and motor function, due to VR intervention.

Conclusions. Although evidence is limited, it suggests that VR intervention (more than 10 sessions) in stroke patients may have a positive impact on balance, and gait recovery. Better results were obtained when a multimodal approach, combining VR and
conventional physiotherapy, was used. Flexible software seems to adapt better to patients’ requirements, allowing more specific and individual treatments.

Full Text PDF

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[Poster] The Effects of Playing Electronic Musical Instruments During AtHome Rehabilitation on Hemiplegic Upper Limb Function

Objective: To investigate the effects of at-home rehabilitation on the functional improvement of hemiplegic upper limbs by playing electronic musical instruments in stroke patients.

Design: Before-and-after trial, Experimental clinical research.

Setting: Visiting a university hospital as an outpatient.

Participants: Twelve cases of hemiplegic patients, averaging 566.4 years old, having suffered brain stroke and living at home in which 8 to 270 months have passed since onset.

Interventions: An guitar type electrophone and electronic drum were rented out to the homes of the patients as electronic musical instruments; instructions were given to play these instruments once a week as an outpatient for 3 weeks each for a total of 6 weeks, and patients were trained to play the instruments using their paralyzed upper limbs. A set piece was specified weekly, and practice at home of at least 30 minutes a day was imposed. On that basis, changes in motor function and muscle spasms were evaluated. Main Outcome Measure(s): Fugl-Meyer Assessment of motor function items of the upper limb (on a scale of 0 to 66) and Modified Ashworth Scale (MAS).

Results: The Fugl-Meyer Assessment of motor function items of the upper limb improved from an average of 36.17 prior to the experiment to 41.67 following the experiment (p<0.01). Although temporal improvement was confirmed in muscle spasms following the experiment, there was no change in MAS throughout the entire training period.

Conclusions: Rehabilitation of the paralyzed upper limbs by playing music have a good effect for the paralytic improvement of the stroke patients at home.

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