Optimal Accelerometer Placement for Fall Detection of Rehabilitation Patients

Authors

  • Nor Surayahani Suriani Department of Computer Engineering, Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia
  • Fadilla ‘Atyka Nor Rashid Department of Computer Engineering, Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia
  • Nur Yuzailin Yunos Department of Computer Engineering, Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia

Keywords:

Activity Recognition, Home-Based Rehabilitation, Fall Detection, Wearable Sensors,

Abstract

The development of health monitoring system using wearable sensor has lots of potential in the field of rehabilitation and gained lots of attention in the scientific community and industry. The aim and motivation in this field are to focus on the application of wearable technology to monitor elderly or rehab patients in home-based settings to reduce resources and development cost. The wearable sensor such as accelerometer used to emphasise the clinical applications of fall detection during rehabilitation treatment. This paper is intended to determine the optimal sensor placement especially for lower limb activity during rehabilitation exercise. Accelerometer data were collected from three different body locations (hip, thigh, and foot). The lower limb activities involve normal movements such as walking, lifting, sit-to-stand, and stairs. Other unexpected activity such as falls might occur during normal lower limb exercise movement. Then, acceleration data for various lower limbs activities was classified using k-NN and SVM classifier. The result found that the hip was the best location to record data for lower limb activities including when fall occurs.

References

A. Pantelopoulos, N. G. Bourbakis, A Survey on Wearable SensorBased Systems for Health Monitoring and Prognosis, Systems, Man, and Cybernetics, vol. 40, no. 1, pp. 1–12, 2010.

F. Erden, S. Velipasalar, A. Z. Alkar, A. E. Cetin, Sensors in Assisted Living: A survey of signal and image processing methods, IEEE Signal Process. Mag., vol. 33, no. 2, pp. 36–44, 2016.

A. K. Bourke, J. V. O. Brien, G. M. Lyons, Evaluation of a thresholdbased tri-axial accelerometer fall detection algorithm, Gait posture, vol. 26, pp. 194–199, 2007.

Suriani, N.S., Hussain, A., Zulkifley, M.A., Sudden Event Recognition: A Survey, Sensors, vol 13, pp. 9966-9998, 2013.

H. Zheng, N. D. Black, N. D. Harris, Position-sensing technologies for movement analysis in stroke rehabilitation, Med. Biol. Eng. Comput., vol. 43, no. 4, pp. 413–420, 2005.

J. M. Mazilu, S. Blanke, U., Troster, M., Gazit G., Dorfman, M., Hausdorff, A wearable assistant for gait training and rehabilitation in Parkinson’s Disease, IEEE International Conference on Pervasive Computing and Communications Demonstrations, 2014, pp. 135–137.

R. Bartalesi, F. Lorussi, M. Tesconi, A. Tognetti, G. Zupone, D. D. Rossi, Wearable kinesthetic system for capturing and classifying upper limb gesture, WH, pp. 535–536, 2005.

Pan, J.I., Chung, H.W., Huang J.J., Intelligent shoulder joint home-based self-rehabilitation monitoring system, Int. J. Smart Home, pp. 395–404, 2013.

Dobkin B, Xu X, Batalin M, Thomas S, Kaiser W., Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke, Stroke, vol. 42, pp 46– 50,2011.

Ye X., Chen G., Cao Y., Automatic Eating Detection using Head-mount and Wrist-worn Accelerometers, Int. Conf. on E-health Networking, Application & Services, pp. 578-581, 2015.

Dean C, Rissel C, Sherrington C, et al., Exercise to enhance mobility and prevent falls after stroke: The community stroke club randomized trial, Neurorehabilitation Neural Repair, vol. 26, pp. 46–57, 2012.

E. S. Sazonov, G. Fulk, N. Sazonova, S. Schuckers, Automatic recognition of postures and activities in stroke patients, Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC 2009, pp. 2200–2203, 2009.

A. Khan, Y. Lee, S. Lee, T. Kim, A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer, IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 5, pp. 1166–1172, 2010.

G. Uslu, H. Dursunogle, O. Altun, S. Baydere, Human Activity Monitoring with Wearable Sensor and Hybrid Classifiers, Int. Journal of Computer Information Systems and Industrial Management Applications, vol. 5, pp. 345-353, 2013.

G. Z. Yang , M. Yacoub, Body Sensor Networks., Springer, 2006.

A. Khan, Y. Lee, S. Lee, T. Kim, Optimizing the F-Measure in MultiLabel Classification: Plug-in Rule Approach versus Structured Loss Minimization, Int. Conf. on Machine Learning, pp. 1–9, 2013.

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Published

2018-07-04

How to Cite

Suriani, N. S., Nor Rashid, F. ‘Atyka, & Yunos, N. Y. (2018). Optimal Accelerometer Placement for Fall Detection of Rehabilitation Patients. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 25–29. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4344