Posts Tagged Performance evaluation

[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation


Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available:

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from [Online]. Available:

via Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation – IEEE Conference Publication

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[Abstract] Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game – IEEE Conference Publication


This paper presented a game-based rehabilitation of the upper limb after stroke. We designed and developed a game for supporting stroke patients to have an exercise their arms, and the game had functions for recording their playing and showing a performance report. The performance report can infer the progress of bilateral uppper-limb rehabilitation and use for comparing among patient cases. This is because the game used a Kinect device to detect the arm movements in aspect of precision and speed.


1. L. Anderson, G. A. Sharp, R. J. Norton, H. Dalal, S. G. Dean, K. Jolly, A. Cowie, A. Zawada, R. S. Taylor, “Home-based versus centre-based cardiac rehabilitation”, The Cochrane Library, 2017.

2. K. Thomson, A. Pollock, C. Bugge, M. C. Brady, “Commercial gaming devices for stroke upper limb rehabilitation: a survey of current practice”, Disability and Rehabilitation: Assistive Technology, vol. 11, no. 6, pp. 454-461, 2016.

3. L. Y. Joo, T. S. Yin, D. Xu, E. Thia, P. F. Chia, C. W. K. Kuah, K. K. He, “A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke”, Journal of rehabilitation medicine, vol. 42, no. 5, pp. 437-441, 2010.

4. K. Price, “Health promotion and some implications of consumer choice”, Journal of nursing management, vol. 14, no. 6, pp. 494-501, 2006.

5. J. A. M. Bravo, P. Paliyawan, T. Harada, R. Thawonmas, “Intelligent assistant for providing instructions and recommending motions during full-body motion gaming”, Consumer Electronics (GCCE) 2017 IEEE 6th Global Conference on. IEEE, pp. 1-2, 2017.


via Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game – IEEE Conference Publication


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[Abstract + References] Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication


The paper presents a user study to compare the performance of two rehabilitation robotic systems, called HomeRehab and PupArm. The first one is a novel tele-rehabilitation system for delivering therapy to stroke patients at home and the second one has been designed and developed to provide rehabilitation therapy to patients in clinical settings. Nine patients with different neurological disorders participated in the study. The patients performed 16 movements with each robotic platform and after that they filled a usability survey. Moreover, to evaluate the patient’s performance with each robotic device, 8 movement parameters were computed from each trial and for the two robotic devices. Based on the analysis of subjective assessments of usability and the data acquired objectively by the robotic devices, we can conclude that the performance and user experience with both systems are very similar. This finding will be the base of more extensive studies to demonstrate that home-therapy with HomeRehab could be as efficient as therapy in clinical settings assisted by PupArm robot.


1. WHO global report. Preventing Chronic Diseases: A Vital Investment, World Health Organization, 2005.

2. J. Mackay, G. A. Mensah, The Atlas of Heart Disease and Stroke, Geneva, Switzerland:World Health Organization, 2004.

3. D. S. Nichols-Larsen, P. C. Clark, A. Zeringue, A. Greenspan, S. Blanton, “Factors Influencing Stroke Survivors Quality of Life during Subacute Recovery”, Stroke, vol. 36, pp. 14801484, 2005.

4. P. Langhorne, F. Coupar, A. Pollock, “Motor Recovery after Stroke: a Systematic Review”, The Lancet Neurology, vol. 8, no. 8, pp. 741754, 2009.

5. C. R. Carnigan, H. I. Krebs, “Telerehabilitation Robotics: Bright Lights Big Future?”, Journal of Rehabilitation Research and Development, vol. 43, no. 5, pp. 695-710, 2006.

6. K. J. Ottenbacher, P. M. Smith, S. B. Illig, R. T. Linn, G. V. Ostir, C. V. Granger, “Trends in Length of Stay Living Setting Functional Outcome and Mortality following Medical Reha-bilitation”, JAMA, vol. 292, no. 14, pp. 1687-1695, 2004.

7. L. Richards, C. Hanson, M. Wellborn, A. Sethi, “Driving Motor Recovery after Stroke”, Topics in Stroke Rehabilitation, vol. 15, no. 5, pp. 397411, 2008.

8. S. M. Linder, A. B. Rosenfeldt, A. Reiss, S. Buchanan, K. Sahu, C. R. Bay, S. L. Wolf, J. L. Alberts, “The Home Stroke Rehabilitation and Monitoring System Trial: A Randomized Controlled Trial”, International Journal of Stroke, vol. 8, no. 1, pp. 1747-4949, 2013.

9. T. Larsen, T. S. Olsen, J. Sorensen, “Early Home-Supported Discharge of Stroke Patients: A Health Technology Assessment”, International Journal of Technology Assessment in Health Care, vol. 22, no. 3, pp. 313-320, 2006.

10. Ifiaki Díaz, José María Catalan, Francisco Javier Badesa, Xabier Justo, Luis Daniel Lledo, Axier Ugartemendia, Jorge juan Gil, Jorge Díez, Nicolás García-Aracil, Development of a robotic device for post-stroke home tele-rehabilitation. Advances in Mechanical Engineering, vol. 10, no. 1, pp. 1-8, 2018.

11. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, J. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

12. R. Likert, G. M. Maranell, “A method of constructing an attitude scale” in Scaling: A Sourcebook for Behavioral Scientists, Chicago, IL:Aldine Publishing, pp. 233243, 1974.

13. H. J. Krebs, N. Hogan, M. L. Aisen, B. T. Volpe, “Robot-aided neurorehabilitation”, IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 1, pp. 75-87, Mar 1998.

14. Franciso J Badesa, Ana Llinares, Ricardo Morales, Nicolas Garcia-Aracil, Jose M Sabater, Carlos Perez-Vidal, “Pneumatic planar rehabilitation robot for post-stroke patients”, Biomedical Engineering: Applications Basis and Communications, vol. 26, no. 2, pp. 1450025, 2014.

15. D. Lledo Luis, A. Diez Jorge, Bertomeu-Motos Arturo, Ezquerro Santiago, J. Badesa Francisco, M. Sabater-Navarro Jose, Garca-Aracil Nicolas, “A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients”, Frontiers in Aging Neuroscience, vol. 8, pp. 205, 2016.

16. A. Llinares, F. J. Badesa, R. Morales, N. Garcia-Aracil, J. Sabater, E. Fernandez, “Robotic assessment of the influence of age on upper-limb sensorimotor function”, Clin. Interv. Aging, vol. 8, pp. 879, 2013.

17. D. S. Dunn, Statistics and data analysis for the behavioral sciences, New York, NY, US:McGraw-Hill, 2001.

18. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, I. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

19. AM Coderre, AA Zeid, SP Dukelow et al., “Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching”, Neurorehabil Neural Repair., vol. 24, no. 6, pp. 528541, 2010.

via Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication

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[Abstract] Electromyography Based Orthotic Arm and Finger Rehabilitation System


Electromyography (EMG), a technique used to analyze and record electric current produced by skeletal muscles, has been used to control replacement limbs, and diagnose muscle irregularities. In this work, an EMG based system comprising of an orthotic arm and finger device to aid in muscle rehabilitation, is presented. As the user attempts to contract their bicep or forearm muscles, the system senses the change in the EMG signals and in turn triggers the motors to assist with flexion and extension of the arm and fingers. As brain is a major factor for muscle growth, mental training using motor imagery was incorporated into the system. Subjects underwent mental training to show the capability of muscle growth. The measured data reveals that the subjects were able to compensate for the loss of muscle growth, due to shorter physical training sessions, with mental training. Subjects were then tested using the orthotic arm and finger rehabilitation device with motor imagery. The findings also showed a positive increase in muscle growth using the rehabilitation system. Based on the experimental results, the EMG rehabilitation system presented in this paper has the potential to increase muscle strength and improve the recovery rate for muscle injuries, partial paralysis, or muscle irregularities.

via Electromyography Based Orthotic Arm and Finger Rehabilitation System – IEEE Conference Publication

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[Abstract] Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring


This paper investigates Kinect device application during rehabilitation of people with an ischemic stroke. There are many similar application using Kinect as a tool during rehabilitation. This paper is focused on measurement of Kinect’s spatial accuracy and proposition of body states and exercises according to the Motor assessment scale for stroke (MAS). The system observes the whole rehabilitation process and objectively compares ranges of movement during each exercise. Angles between limbs are computed in the skeletal body joints projection to three anatomical planes, which enables a better insight to subject performance. The system is easily implemented with a consumer-grade computer and a low-cost Kinect device. Selected exercises are presented together with the angles evolution, body states recognition and the MAS Scale after the stroke classification.

Source: Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring – IEEE Xplore Document

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[ARTICLE] User-centred input for a wearable soft-robotic glove supporting hand function in daily life


Many stroke patients and elderly have a reduced hand function, resulting in difficulties with independently performing activities of daily living (ADL). Assistive technology is a promising alternative to support the upper limb in performing ADL. To avoid device abandonment, end-users should be involved early in the design and development phase to identify user requirements for assistive technology.

The present study applies a user-centred approach to identify user requirements for wearable soft-robotic gloves targeted at physical support of hand function during ADL for elderly and stroke patients.

Elderly, stroke patients and healthcare professionals, participating in focus groups, specified requirements regarding:

  1. activities that need support of assistive technology,
  2. design of wearable robotic devices for hand support, and
  3. application of assistive technology as training tool at home.

Assistive technology for the support of the hand is considered valuable by users for assisting ADL, but only if the device is wearable, compact, lightweight, easy to use, quickly initialized, washable and only supports the particular function(s) that an individual need(s) assistance with, without taking over existing function(s) from the user.

Source: IEEE Xplore Abstract – User-centred input for a wearable soft-robotic glove supporting hand function in daily life

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