A Wearable Sensor Network for Gait Analysis: A Six-Day Experiment of Running Through the Desert

This paper presents a new system for analysis of walking and running gaits. The system is based on a network of wireless nodes with various types of embedded sensors. It has been designed to allow long-term recording in outdoor environments and was tested during the 2010 “Sultan Marathon des Sables” desert race. A runner was fitted with the sensory network for six days of the competition. Although technical problems have limited the amount of data recorded, the experiment was nevertheless successful: the system did not interfere with the runner, who finished with a high ranking, the concept was validated and high quality data were acquired. It should be noted that the loss of some of the measurements was mainly due to problems with the cable connectors between the nodes and batteries. In this paper, we describe the technical aspects of the system developed, the experimental conditions under which it was validated, and give examples of the data obtained with some preliminary processing.

[1]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[2]  Wen-Bing Horng,et al.  Driver fatigue detection based on eye tracking and dynamk, template matching , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[3]  Joseph A. Paradiso,et al.  Gait Analysis Using a Shoe-Integrated Wireless Sensor System , 2008, IEEE Transactions on Information Technology in Biomedicine.

[4]  Ronald R. Yager,et al.  OWA aggregation over a continuous interval argument with applications to decision making , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Deborah Estrin,et al.  Coping with irregular spatio-temporal sampling in sensor networks , 2004, CCRV.

[6]  Darwin Gouwanda,et al.  Emerging Trends of Body-Mounted Sensors in Sports and Human Gait Analysis , 2008 .

[7]  Bernard Espiau,et al.  Signal-based segmentation of human locomotion using embedded sensor network , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Yili Liu,et al.  Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems , 2006, TCHI.

[9]  C He,et al.  Evaluation of the critical value of driving fatigue based on the fuzzy sets theory. , 1993, Environmental research.

[10]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Wayne D. Gray.  Integrated Models of Cognitive Systems , 2007, Oxford series on cognitive models and architectures.

[12]  Kay Römer,et al.  Time Synchronization and Calibration in Wireless Sensor Networks , 2005, Handbook of Sensor Networks.

[13]  Prabir Bhattacharya,et al.  A driver fatigue recognition model based on information fusion and dynamic Bayesian network , 2010, Inf. Sci..

[14]  Yili Liu,et al.  Development of an Adaptive Workload Management System Using the Queueing Network-Model Human Processor (QN-MHP) , 2008, IEEE Transactions on Intelligent Transportation Systems.

[15]  G. Hamouda,et al.  Neural network model for truck driver fatigue accident detection , 1995, Proceedings 1995 Canadian Conference on Electrical and Computer Engineering.

[16]  Bernard Espiau,et al.  Gait spectral index (GSI): a new quantification method for assessing human gait , 2010 .

[17]  A. Hof,et al.  Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. , 2003, Gait & posture.

[18]  Qiang Ji,et al.  Active affective State detection and user assistance with dynamic bayesian networks , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[20]  Qiang Ji,et al.  A probabilistic framework for modeling and real-time monitoring human fatigue , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[21]  Wiebren Zijlstra,et al.  Accelerometry based assessment of gait parameters in children. , 2006, Gait & posture.

[22]  Elyes Ben Hamida,et al.  Neighbor Discovery in Multi-hop Wireless Networks: Evaluation and Dimensioning with Interferences Considerations , 2008, Discret. Math. Theor. Comput. Sci..

[23]  Jindong Tan,et al.  BioLogger: A wireless physiological sensing and logging system with applications in poultry science , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.