A New Force Myography-Based Approach for Continuous Estimation of Knee Joint Angle in Lower Limb Amputees and Able-Bodied Subjects

In this paper, we present a new method for estimating knee joint angle using force myography. The technique utilized force myogram signals from thigh muscles while subjects walked on a treadmill at different speeds, i.e., slow, medium, fast, and run. An eight-channel in-house force myography (FMG) data acquisition system was developed to collect the data wirelessly from seven healthy subjects and a transfemoral amputee. An artificial neural network was employed to estimate the knee joint angle from force myogram signals. The root-mean-square error across the healthy subjects was 6.9±1.5° at slow (1.5 km/hr), 6.5±1.3° at medium (4 km/hr), 7.4±2.2° at fast (6 km/hr) speeds, and 8.1±2.2° while running (8 km/hr). The root-mean-square error, across the trials, for the transfemoral amputee was 4.0±1.2° at slow (1 km/hr), 3.2±0.6° at medium (2 km/hr) and 3.8±0.9° at fast (3 km/hr) speeds. The proposed approach is useful in real-time gait analysis. The system is easily wearable, convenient in out-door use, portable, and commercially viable.

[1]  Sneh Anand,et al.  ANFIS based knee angle prediction: An approach to design speed adaptive contra lateral controlled AK prosthesis , 2011, Appl. Soft Comput..

[2]  Björn Eskofier,et al.  Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors , 2013, BODYNETS.

[3]  G. Naik,et al.  Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[5]  Steven J Stanhope,et al.  Changes in knee joint function over a wide range of walking speeds. , 1997, Clinical biomechanics.

[6]  Yu Liu,et al.  Lower extremity joint torque predicted by using artificial neural network during vertical jump. , 2009, Journal of biomechanics.

[7]  A. Thilmann,et al.  Biomechanical changes at the ankle joint after stroke. , 1991, Journal of neurology, neurosurgery, and psychiatry.

[8]  Don A. Yungher,et al.  Surface muscle pressure as a measure of active and passive behavior of muscles during gait. , 2011, Medical engineering & physics.

[9]  Yoshiyuki Sankai,et al.  Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients , 2013, BMC Neurology.

[10]  Franco Franchignoni,et al.  Reliability, validity, and responsiveness of the locomotor capabilities index in adults with lower-limb amputation undergoing prosthetic training. , 2004, Archives of physical medicine and rehabilitation.

[11]  B. Munoz,et al.  Falls and Fear of Falling: Which Comes First? A Longitudinal Prediction Model Suggests Strategies for Primary and Secondary Prevention , 2002, Journal of the American Geriatrics Society.

[12]  K Aminian,et al.  Ambulatory assessment of 3D ground reaction force using plantar pressure distribution. , 2010, Gait & posture.

[13]  Carlo Menon,et al.  A Case Study of a Force-myography Controlled Bionic Hand Mitigating Limb Position Effect , 2017 .

[14]  Carlo Menon,et al.  A Wearable Gait Phase Detection System Based on Force Myography Techniques , 2018, Sensors.

[15]  Xiaorong Guan,et al.  Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis , 2019, Electronics.

[16]  Jianda Han,et al.  Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model. , 2017, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[17]  F. Prince,et al.  Locomotor Strategies in Obese and Non‐obese Children , 2006, Obesity.

[18]  Demetris Stathakis,et al.  How many hidden layers and nodes? , 2009 .

[19]  Billur Barshan,et al.  Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors , 2017, Sensors.

[20]  Anke Xue,et al.  Surface Electromyography Based Estimation of Knee Joint Angle by Using Correlation Dimension of Wavelet Coefficient , 2019, IEEE Access.

[21]  Xiaodong Zhang,et al.  Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks , 2018, Biomed. Signal Process. Control..

[22]  D. Lefeber,et al.  A Knee–Ankle–Foot Orthosis to Assist the Sound Limb of Transfemoral Amputees , 2019, IEEE Transactions on Medical Robotics and Bionics.

[23]  Deepak Joshi,et al.  A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking , 2018, IEEE Transactions on Instrumentation and Measurement.

[24]  Panagiotis K. Artemiadis,et al.  Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography , 2014, Front. Neurorobot..

[25]  P. Manns,et al.  Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. , 1999, Archives of physical medicine and rehabilitation.

[26]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[27]  Carlo Menon,et al.  Continuous Prediction of Finger Movements Using Force Myography , 2016 .

[28]  Diana Hodgins,et al.  Inertial sensor-based knee flexion/extension angle estimation. , 2009, Journal of biomechanics.

[29]  Giancarlo Ferrigno,et al.  A Personalized Multi-Channel FES Controller Based on Muscle Synergies to Support Gait Rehabilitation after Stroke , 2016, Front. Neurosci..

[30]  Kamiar Aminian,et al.  A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes , 2005, IEEE Transactions on Biomedical Engineering.

[31]  Hao Yu,et al.  Selection of Proper Neural Network Sizes and Architectures—A Comparative Study , 2012, IEEE Transactions on Industrial Informatics.

[32]  Carlo Menon,et al.  Wearable step counting using a force myography-based ankle strap , 2017, Journal of rehabilitation and assistive technologies engineering.

[33]  A. Vianello,et al.  Functional status of adults with cerebral palsy and implications for treatment of children , 2001, Developmental medicine and child neurology.

[34]  Karen J. Nolan,et al.  Preliminary Validation of a Cable-Driven Powered Ankle–Foot Orthosis With Dual Actuation Mode , 2019, IEEE Transactions on Medical Robotics and Bionics.

[35]  Deepak Joshi,et al.  Analysis of force myography based locomotion patterns , 2019, Measurement.

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

[37]  M. Abdoli-Eramaki,et al.  The effect of perspiration on the sEMG amplitude and power spectrum. , 2012, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[38]  P. Vieregge,et al.  Gait quantitation in Parkinson's disease — locomotor disability and correlation to clinical rating scales , 2005, Journal of Neural Transmission.

[39]  Erik J Scheme,et al.  A proportional control scheme for high density force myography , 2018, Journal of neural engineering.

[40]  Kenji Suzuki,et al.  Standing Mobility Device With Passive Lower Limb Exoskeleton for Upright Locomotion , 2018, IEEE/ASME Transactions on Mechatronics.

[41]  Alessandro Tognetti,et al.  Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life , 2015, Sensors.

[42]  J. Burdick,et al.  Implications of Assist-As-Needed Robotic Step Training after a Complete Spinal Cord Injury on Intrinsic Strategies of Motor Learning , 2006, The Journal of Neuroscience.

[43]  Shiv Dutt Joshi,et al.  Force Myography Based Novel Strategy for Locomotion Classification , 2018, IEEE Transactions on Human-Machine Systems.

[44]  Force Myography across Socket Material , 2019 .

[45]  P. Gallina,et al.  Design, Implementation and Clinical Tests of a Wire-Based Robot for Neurorehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  Feng Zhang,et al.  sEMG-based continuous estimation of joint angles of human legs by using BP neural network , 2012, Neurocomputing.

[47]  A. Thorstensson,et al.  Adaptations to changing speed in human locomotion: speed of transition between walking and running. , 1987, Acta physiologica Scandinavica.

[48]  Jingang Yi,et al.  Real-Time Intended Knee Joint Motion Prediction by Deep-Recurrent Neural Networks , 2019, IEEE Sensors Journal.

[49]  S. Karsoliya,et al.  Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .

[50]  Tao Liu,et al.  Ambulatory measurement and analysis of the lower limb 3D posture using wearable sensor system , 2009, 2009 International Conference on Mechatronics and Automation.