Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body

Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects’ wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state). Their ages were 27.2 ± 3.3 years and their body mass index (BMI) is 25.11 ± 2.6 Kg/m2. Classification results demonstrated a high validity showing precision (a positive predictive value) and recall (sensitivity) of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients.

[1]  Roger J. R. Levesque,et al.  Obesity and Overweight , 2011 .

[2]  Angelo M. Sabatini,et al.  Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers , 2010, Sensors.

[3]  Ludmila I. Kuncheva,et al.  Multiple Classifier Systems , 2004 .

[4]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[5]  Kenji Mase,et al.  Activity and Location Recognition Using Wearable Sensors , 2002, IEEE Pervasive Comput..

[6]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[7]  T. Vos,et al.  Cost-effectiveness of diet and exercise interventions to reduce overweight and obesity , 2011, International Journal of Obesity.

[8]  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.

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

[10]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Belén Melián-Batista,et al.  Solving feature subset selection problem by a Parallel Scatter Search , 2006, Eur. J. Oper. Res..

[12]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[13]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[14]  Guang-Zhong Yang,et al.  Sensor Positioning for Activity Recognition Using Wearable Accelerometers , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[15]  Roger O. Smith,et al.  Feature Extraction Method for Real Time Human Activity Recognition on Cell Phones , 2011 .

[16]  Michael Luck,et al.  Research and Development in Intelligent Systems XXVII , 2011 .

[17]  Koichi Sagawa,et al.  Measurement of three-dimensional posture and trajectory of lower body during standing long jumping utilizing body-mounted sensors , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  C. Himes,et al.  Effect of Obesity on Falls, Injury, and Disability , 2012, Journal of the American Geriatrics Society.

[19]  T Togawa,et al.  Classification of waist-acceleration signals in a continuous walking record. , 2000, Medical engineering & physics.

[20]  Jo Mitchell,et al.  Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study , 2003, Public Health Nutrition.

[21]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[22]  Clifford Qualls,et al.  Weight loss, exercise, or both and physical function in obese older adults. , 2011, The New England journal of medicine.

[23]  Tapio Seppänen,et al.  Recognizing human motion with multiple acceleration sensors , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[24]  Merryn J Mathie,et al.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement , 2004, Physiological measurement.

[25]  The InterAct Consortium,et al.  Validity of a short questionnaire to assess physical activity in 10 European countries , 2012 .

[26]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[27]  Tanu Sharma,et al.  A novel feature extraction for robust EMG pattern recognition , 2016, Journal of medical engineering & technology.

[28]  Tadahiro Kuroda,et al.  Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[29]  Didier Stricker,et al.  Creating and benchmarking a new dataset for physical activity monitoring , 2012, PETRA '12.

[30]  Tom Chau,et al.  The effect of accelerometer location on the classification of single-site forearm mechanomyograms , 2010, Biomedical engineering online.

[31]  David Minnen,et al.  Recognizing Soldier Activities in the Field , 2007, BSN.

[32]  Young-Koo Lee,et al.  Semi-Markov conditional random fields for accelerometer-based activity recognition , 2010, Applied Intelligence.

[33]  Fred W. Glover,et al.  Principles of scatter search , 2006, Eur. J. Oper. Res..

[34]  L. Shammas,et al.  Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study) , 2014, Biomedical engineering online.

[35]  Juan José Rodríguez Diez,et al.  An Experimental Study on Rotation Forest Ensembles , 2007, MCS.

[36]  Michael G. Madden,et al.  An Ensemble Dynamic Time Warping Classifier with Application to Activity Recognition , 2010, SGAI Conf..

[37]  Doreen Meier,et al.  Fundamentals Of Neural Networks Architectures Algorithms And Applications , 2016 .

[38]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[39]  Angela Kong,et al.  Effect of Diet and Exercise, Alone or Combined, on Weight and Body Composition in Overweight‐to‐Obese Postmenopausal Women , 2012, Obesity.

[40]  Lloyd A. Smith,et al.  Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper , 1999, FLAIRS.

[41]  Hikaru Inooka,et al.  RECOGNITION OF DAILY AMBULATORY MOVEMENTS UTILIZING ACCELEROMETER AND BAROMETER , 2004 .

[42]  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).

[43]  Marimuthu Palaniswami,et al.  Motor recovery monitoring using acceleration measurements in post acute stroke patients , 2013, Biomedical engineering online.

[44]  Kazuhiro Kosuge,et al.  Motion Control of Passive Intelligent Walker Using Servo Brakes , 2007, IEEE Transactions on Robotics.

[45]  Pornchai Phukpattaranont,et al.  A Novel Feature Extraction for Robust EMG Pattern Recognition , 2009, ArXiv.

[46]  Kazuhiro Kosuge,et al.  A Control Approach Based on Passive Behavior to Enhance User Interaction , 2007, IEEE Transactions on Robotics.

[47]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[48]  D. Bassett,et al.  The technology of accelerometry-based activity monitors: current and future. , 2005, Medicine and science in sports and exercise.

[49]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[50]  Yeh-Liang Hsu,et al.  A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring , 2010, Sensors.

[51]  Didier Stricker,et al.  Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.

[52]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[53]  Salvatore Panico,et al.  Validity of a short questionnaire to assess physical activity in 10 European countries , 2011, European Journal of Epidemiology.

[54]  Sebastian Thrun,et al.  Learning user models of mobility-related activities through instrumented walking aids , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[55]  Weihua Sheng,et al.  Human daily activity recognition in robot-assisted living using multi-sensor fusion , 2009, 2009 IEEE International Conference on Robotics and Automation.

[56]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[57]  G M Lyons,et al.  A description of an accelerometer-based mobility monitoring technique. , 2005, Medical engineering & physics.