Wireless kinematic body sensor network for low-cost neurotechnology applications “in-the-wild”

We present an ultra-portable and low-cost body sensor network (BSN), which enables wireless recording of human motor movement kinematics and neurological signals in unconstrained, daily-life environments. This is crucial as activities of daily living (ADL) and thus metrics of everyday movement enable us to diagnose motor and neurological disorders in the patients context, and not artificial laboratory settings. Moreover, ADL kinematics inform us how to control neuroprosthetics and brain-machine interfaces in a natural manner. Our system uses a network of battery-powered embedded micro-controllers, to capture data from motion sensors placed all over the human body and wireless connectivity to stream process data in real time at 100 Hz. Our prototype compares well against two gold-standard measures, a ground-truth motion tracking system and high-end motion capture suit as reference. At 2.5% of the cost, performance in capturing natural joint kinematics are accurate R2 = 0.89 and precise RMSE = 1.19°. The system's low-cost (approximately $100 per unit), wireless capability, low weight and millimetre-scale size allow subjects to be unconstrained in their actions while having the sensors attached to everyday clothing. These features establish our system's usefulness in clinical studies, risk-group monitoring, neuroscience and neuroprosthetics.

[1]  Nigel Sim,et al.  The head mouse — Head gaze estimation "In-the-Wild" with low-cost inertial sensors for BMI use , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[2]  William Harwin,et al.  Measuring motion with kinematically redundant accelerometer arrays: Theory, simulation and implementation , 2013 .

[3]  Guang-Zhong Yang,et al.  BODY SENSOR NETWORKS - RESEARCH CHALLENGES AND OPPORTUNITIES , 2007 .

[4]  Henry Been-Lirn Duh,et al.  A wearable, self-calibrating, wireless sensor network for body motion processing , 2008, 2008 IEEE International Conference on Robotics and Automation.

[5]  A. Faisal,et al.  Near Optimal Combination of Sensory and Motor Uncertainty in Time During a Naturalistic Perception-Action Task , 2008, Journal of neurophysiology.

[6]  W W Abbott,et al.  Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain–machine interfaces , 2012, Journal of neural engineering.

[7]  Aldo A. Faisal,et al.  The Manipulative Complexity of Lower Paleolithic Stone Toolmaking , 2010, PloS one.

[8]  Tom Chau,et al.  The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements , 2010, Journal of NeuroEngineering and Rehabilitation.

[9]  A. Aldo Faisal Studying channelopathies at the functional level using a system identification approach , 2008 .

[10]  A. Aldo Faisal,et al.  Real-time movement prediction for improved control of neuroprosthetic devices , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[11]  Constantinos Gavriel,et al.  Robust, ultra low-cost MMG system with brain-machine-interface applications , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[12]  I. Alber,et al.  Freestanding copper nanocones for field emission by ion-track technology and electrodeposition , 2011, 2011 24th International Vacuum Nanoelectronics Conference.

[13]  Hassan Ghasemzadeh,et al.  A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities , 2010, IEEE Transactions on Information Technology in Biomedicine.

[14]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[15]  William W. Abbott,et al.  Large-field study of ultra low-cost, non-invasive task level BMI , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[16]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[17]  A. Aldo Faisal,et al.  Stochastic Simulation of Neurons, Axons, and Action Potentials , 2009 .

[18]  A. Compston,et al.  Multiple sclerosis. , 2002, Lancet.

[19]  H. Vanharanta,et al.  Pocket-size, portable surface EMG device in the differentiation of low back pain patients , 2004, European Spine Journal.

[20]  Nunzio Abbate,et al.  Development of a MEMS based wearable motion capture system , 2009, 2009 2nd Conference on Human System Interactions.