A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach

This paper proposes a robotic hand rehabilitation device for grasp training. The device is designed for stroke patients to train and recover their hand grasp function in order to undertake activities of daily living (ADL). The device consists of a control unit, two small actuators, an infrared (IR) sensor, and pressure sensors in the grasp handle. The advantages of this device are that it is small in size, inexpensive, and available for use at home without specialist's supervision. In addition, a novel patient-driven strategy based on the patient's movement intention detected by the pressure sensors without bio-signals is introduced. Once the system detects a patient's movement intention, it triggers the robotic device to move the patient's hand to form the normal grasping behavior. This strategy may encourage stroke patients to participate in rehabilitation training to recover their hand grasp function and it may also enhance neural plasticity. A user study was conducted in order to investigate the usability, acceptability, satisfaction, and suggestions for improvement of the proposed device. The results of this survey included positive reviews from therapists and a stroke patient. In particular, therapists expected that the proposed patient-driven mode can motivate patients for their rehabilitation training and it can be effective to prevent a compensational strategy in active movements. It is expected that the proposed device will assist stroke patients in restoring their grasp function efficiently.

[1]  Y. Hsieh,et al.  Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: a randomized controlled trial , 2012, Clinical rehabilitation.

[2]  George A. Mensah,et al.  The atlas of heart disease and stroke , 2005 .

[3]  R Riener,et al.  Patient-driven control of FES-supported standing up: a simulation study. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  K. Y. Tong,et al.  An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[5]  Lauri Bishop,et al.  A pilot study of robotic-assisted exercise for hand weakness after stroke , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[6]  K J Sullivan,et al.  Activity-Dependent Factors Affecting Poststroke Functional Outcomes , 2001, Topics in stroke rehabilitation.

[7]  Y. Hsieh,et al.  activity in patients with chronic stroke: a randomized controlled trial Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm , 2011 .

[8]  Sehyung Park,et al.  Enhanced hand rehabilitation using a haptic interface , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[9]  Etienne Burdet,et al.  Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[10]  N B Lincoln,et al.  Effect of severity of arm impairment on response to additional physiotherapy early after stroke , 1999, Clinical rehabilitation.

[11]  J. Schaechter Motor rehabilitation and brain plasticity after hemiparetic stroke , 2004, Progress in Neurobiology.

[12]  R. Riener,et al.  Human-centered rehabilitation robotics , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[13]  D. Nelson,et al.  The use of a game to promote arm reach in persons with traumatic brain injury. , 1993, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[14]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[15]  Paolo Bonato,et al.  Variable Resistance Hand Device using an electro-rheological fluid damper , 2009, World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[16]  R. Teasell,et al.  An Evidence-Based Review of Stroke Rehabilitation , 2003, Topics in stroke rehabilitation.

[17]  Vera Kaiser,et al.  First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier , 2011, Front. Neurosci..

[18]  L. Cohen,et al.  Neuroplasticity in the context of motor rehabilitation after stroke , 2011, Nature Reviews Neurology.

[19]  Cuntai Guan,et al.  A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface , 2011, Clinical EEG and neuroscience.