Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities

BackgroundBody motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system which can predict different postures of the upper-extremities by classifying force myographic (FMG) signals of the forearm in real-time.MethodsAn easy to use force sensor resistor (FSR) strap to extract the upper-extremities FMG signals was prototyped. The FSR strap was designed to be placed on the proximal portion of the forearm and capture the activities of the main muscle groups with eight force input channels. The non-kernel based extreme learning machine (ELM) classifier with sigmoid based function was implemented for real-time classification due to its fast learning characteristics. A test protocol was designed to classify in real-time six upper-extremities postures that are needed to successfully complete a drinking task, which is a functional exercise often used in constraint-induced movement therapy. Six healthy volunteers participated in the test. Each participant repeated the drinking task three times. FMG data and classification results were recorded for analysis.ResultsThe obtained results confirmed that the FMG data captured from the FSR strap produced distinct patterns for the selected upper-extremities postures of the drinking task. With the use of the non-kernel based ELM, the postures associated to the drinking task were predicted in real-time with an average overall accuracy of 92.33% and standard deviation of 3.19%.ConclusionsThis study showed that the proposed wearable FSR strap was able to detect eight FMG signals from the forearm. In addition, the implemented ELM algorithm was able to correctly classify in real-time six postures associated to the drinking task. The obtained results therefore point out that the proposed system has potential for providing instant feedback during functional rehabilitation exercises.

[1]  Hong Liu,et al.  Biomechatronic approach to a multi-fingered hand prosthesis , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[2]  Carlo Menon,et al.  Surface EMG pattern recognition for real-time control of a wrist exoskeleton , 2010, Biomedical engineering online.

[3]  Paul Lukowicz,et al.  Using FSR based muscule activity monitoring to recognize manipulative arm gestures , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[4]  I. Olkin,et al.  Using pedometers to increase physical activity and improve health: a systematic review. , 2007, JAMA.

[5]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[6]  Dapeng Yang,et al.  Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map , 2012 .

[7]  A. Gordon.  To constrain or not to constrain, and other stories of intensive upper extremity training for children with unilateral cerebral palsy , 2011, Developmental medicine and child neurology.

[8]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[9]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[10]  Valerie M Pomeroy,et al.  The effects of increased dose of exercise-based therapies to enhance motor recovery after stroke: a systematic review and meta-analysis , 2010, BMC medicine.

[11]  Paul Lukowicz,et al.  Sensing muscle activities with body-worn sensors , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[12]  P. Dario,et al.  Artificial neural network model of the mapping between electromyographic activation and trajectory patterns in free-arm movements , 2003, Medical and Biological Engineering and Computing.

[13]  K. Croteau,et al.  A Preliminary Study on the Impact of a Pedometer-Based Intervention on Daily Steps , 2004, American journal of health promotion : AJHP.

[14]  W. Craelius,et al.  Pressure signature of forearm as predictor of grip force. , 2008, Journal of rehabilitation research and development.

[15]  S. Wolf,et al.  Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients , 1989, Experimental Neurology.

[16]  Carlo Menon,et al.  A preliminary investigation assessing the viability of classifying hand postures in seniors , 2011, Biomedical engineering online.

[17]  M. Kreil,et al.  Muscle activity evaluation using force sensitive resistors , 2008, 2008 5th International Summer School and Symposium on Medical Devices and Biosensors.

[18]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  D. Backus.  Exploring the Potential for Neural Recovery After Incomplete Tetraplegia Through Nonsurgical Interventions , 2010, PM & R : the journal of injury, function, and rehabilitation.

[21]  K. M. Anderson,et al.  Application of constraint-induced movement therapy for an individual with severe chronic upper-extremity hemiplegia. , 2003, Physical therapy.

[22]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Panagiotis K. Artemiadis,et al.  A Learning Scheme for Reach to Grasp Movements: On EMG-Based Interfaces Using Task Specific Motion Decoding Models , 2013, IEEE Journal of Biomedical and Health Informatics.

[24]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.