Classification of Walkers Based on Back Angle Measurements Using Wireless Sensor Node

High end technology enabled devices are being used these days to perform classification and analysis of walking styles of athletes and patients for therapeutic applications. Hence it has become an encouraging step to carry out research in related domain. Various sports have significant health benefits which contribute to muscular, heart, and mental health. However, there is high risk of having injuries while playing outdoor sports and are very common in athletes. Relative excessive loading and impulsive impact on the muscle tissues causes almost all type of basic and severe injuries. To study the phenomenon of injury occurrence, avoidance, improvement in training techniques, and therapeutic applications, an open source electronic device has been fabricated using micro-controller and gy-521 sensor module. The designed system was used to study the effect of lower back movement of persons while walking and was able to classify subjects based on the lower back deviation angle. This result shall form the basis of designing customized training sessions suited for athletes to minimize injuries and suggesting of physiotherapy for patients with lower back pain. The designed system can be used as a reliable evaluation device for lower back analysis in various field environments without any constraints. The device could support injury management, performance enhancement, and rehabilitation of lower back pain patients.

[1]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[2]  Petia Radeva,et al.  Human Activity Recognition from Accelerometer Data Using a Wearable Device , 2011, IbPRIA.

[3]  Juha Pärkkä,et al.  Personalization Algorithm for Real-Time Activity Recognition Using PDA, Wireless Motion Bands, and Binary Decision Tree , 2010, IEEE Transactions on Information Technology in Biomedicine.

[4]  E K Antonsson,et al.  The frequency content of gait. , 1985, Journal of biomechanics.

[5]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[6]  Chia-Shang James Chu,et al.  Time Series Segmentation: A Sliding Window Approach , 1995, Inf. Sci..

[7]  W. Xu,et al.  Measuring human joint movement with IMUs: Implementation in custom-made low cost wireless sensors , 2017, 2017 IEEE 15th Student Conference on Research and Development (SCOReD).

[8]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[9]  E. Pietka.  Expert systems in parameter extraction of the ECG signal , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Bernt Schiele,et al.  Analyzing features for activity recognition , 2005, sOc-EUSAI '05.

[11]  Andrea Cereatti,et al.  An objective assessment to investigate the impact of turning angle on freezing of gait in Parkinson's disease , 2017, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[12]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[13]  Daniel Borrajo,et al.  A dynamic sliding window approach for activity recognition , 2011, UMAP'11.

[14]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[15]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[16]  Maury A. Nussbaum,et al.  A “Smart” Undershirt for Tracking Upper Body Motions: Task Classification and Angle Estimation , 2018, IEEE Sensors Journal.

[17]  Hyungmin Kim,et al.  Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Elfed Lewis,et al.  Low cost portable sensor for real-time monitoring of lower back bending , 2017, 2017 25th Optical Fiber Sensors Conference (OFS).

[19]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[20]  B. Celler,et al.  Accelerometry Based Classification of Walking Patterns Using Time-frequency Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[22]  T. Tamura,et al.  Classification of walking pattern using acceleration waveform in elderly people , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[23]  M. N. Nyan,et al.  Classification of gait patterns in the time-frequency domain. , 2006, Journal of biomechanics.