Presenter: Wei-Chen Lin Adviser: Dr. Cheng-Jui Hung Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring Presenter: Wei-Chen Lin Adviser: Dr. Cheng-Jui Hung 2009/2/25

Outline Introduction Paper review Motivation and Purpose Materials and Methods Results Future works References

Introduction Triaxial accelerometer applications Numerical Analysis Experiment Behavior Monitoring systems

Paper review(1) Purpose From: Classification of basic daily movements using a triaxial accelerometer Author(s): Mathie MJ, Celler BG, Lovell NH, et al. Source: Medical & Biological Engineering & Computing   Volume: 42   Issue: 5   Pages: 679-687   Published: SEP 2004 Purpose Advances in miniature sensor and wireless technologies have resulted in interest in the development of systems for monitoring subjects over long periods of time using wearable monitoring units.

Paper review(1) materials and methods Waist-mounted triaxial accelerometer The unit was composed of two orthogonally mounted biaxial accelerometers*(range± 10 g; frequency response: 0-500 Hz) Presented a more systematic approach to classification, based on a formal, hierarchical, decision tree. The algorithms were developed and tested using data collected from 26 normal, healthy subjects (seven female, 19 male; mean age 30.5 years-4-6.3 years standard deviation)

Paper review(1) flow chart TA signal Level 1 Activity? No Level 2 Activity Rest Yes Upright? No Fall? No Lying? No Walking? No Yes Transition? No Inverted: Raise alarm Yes Yes Yes Fall:raise alarm walking Other movement Level 3 Yes Lying face Down? No Sitting? No Upright- Upright? No lying on back? No No Lying on left side? No Upright- Lying? No Yes Yes Yes Yes Lying-lying? No Yes Yes Yes Lying- Upright? Yes Level 4 Yes Upright-to- Lying transition Lying-to-lying transition Lying-to-upright transition sitting standing Lying face down Lying on back Lying on left side Lying on right side TA: Triaxial Accelerometer 三軸加速度器

Paper review(1) results Fig.1 Experimental Statistics tables

Paper review(1) conclusion Using this framework, a classifier for the identification of basic movements, based on a monitoring system consisting of a 686 single, waist-mounted triaxial accelerometer, was developed, in laboratory studies in which 26 subjects performed a specific routine of movements, the system obtained an overall sensitivity of 97.7% and specificity of 98.7% over a data set of 1309 movements.

Paper review(2) Purpose From: Alvarez, J.C.; Gonzalez, R.C.; Alvarez, D.; Lopez, A.M.; Rodriguez-Uria, J.; Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 22-26 Aug. 2007 Page(s):5719 - 5722 Purpose Step lengthcan be computed by means of a biaxial accelerometer and a gyroscope on the sagital plane.

Paper review(2) materials and methods Biaxial accelerometer and a gyroscope Motion is computed, at every stride, by estimating the distance traveled by the foot that swings forward on the air. Fig.2 Experimental device

Paper review(2) experiments results ω:角加速度 θ:角度 Fig. 3-4 One foot displacement, signals from the gyroscope (up), its integration (middle) and the corrected accelerations (down). Computations are made with equations (2) and (3).

Paper review(2) experiments results Fig. 5 Integrating the gyroscope signal of the sagital plane

Paper review(2) conclusion We have presented a method to estimate the step length based on inertial feet attached sensors. Contrary to similar works, a multisensor approach is applied in order to reduce uncertainty and to produce better estimations. An adapted kalman filter based sensor fusion system is proposed. Initial results are encouraging. Ongoing extended field experiments have been designed to validate and generalize the results for a heterogeneous populations and walking conditions.

Motivation and Purpose Monitoring of human movement Measured distance Design platform for measuring the distance To detect the occurrence of falls

Materials and Methods ST LIS302DL Measurement platform 3-Axis range : ± 8g frequency response:100Hz or 400Hz Measurement platform 28cm × 21cm × 3.7cm Microchip APP009 Microchip Dsp30F4011

Measurement platform 可移動方向 APP009 實驗版 尺規刻度 三軸加速度器

Results Fig.6 加速度曲線圖 Fig.7 速度曲線圖

Results Fig. 8距離曲線圖

Results The experimental results show that estimated value is measured 9 cm, the actual measurement of 7.8 centimeters. Error value of 11% Ideal distance number of measure experimental average accuracy 9 cm 54 7.8 cm 89%

Future works Paper review Increase the distance measurement accuracy Data collection and statistics Reduce the board Functional integration

References [1] C.V. Bouten,K. T.Koekkoek, M.Verduin, R.Kodde, and J. D. Janssen, "A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity," IEEE Trans. Biomed. Eng., vol. 44, no. 3, pp. 136–147, 1997. [2] M. J. Mathie, A. C. F. Coster, N. H. Lovell, and B. G. Celler, "A pilot study of long term monitoring of human movements in the home using accelerometry," IEEE Trans. Biomed. Eng., vol. 10, pp. 144–151, 2004. [3] M. Makikawa, D. Murakami, " Development of an ambulatory physical activity and behavior map monitoring system," in 18th Annual Conf. IEEE Engineering in Medicine Biology Soc. Amsterdam, Holland, 1996. [4] M. J.Mathie, N. H. Lovell, A. C. F. Coster, and B. G. Celler, "Determining activity using a triaxial accelerometer," in Proc. 2nd Joint EMBS-BMES Conf., Houston, TX, 2002. [5] M. J.Mathie, B. G. Celler, N.H. Lovell, et al. "Classification of basic daily movements using a triaxial accelerometer," Medicine & Biological Engineering & Computing., vol. 42  pp. 679-687, 2004. [6] W. Zijlstra and A. Hof, "Assessment of spatio-temporal gait parameters from trunk accelerations during human walking," Gait & Posture., vol. 18, pp. 1-10, 2003.

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