A Wearable Activity Recognition Device Using Air-Pressure and IMU Sensors

Human activity recognition (HAR) has received a lot of attention due to its wide applications in recent years, while the improvement of recognition accuracy is seemingly considered to be one of the great challenges in this field. In this paper, a novel wearable device for improving the activity recognition accuracy is proposed based on the different multiple sensors, which simultaneously collects the muscle activity and motion information. The muscular activity is monitored by measuring the air pressure in an air bladder contacting the targeted muscle, while the motion information, such as three-axis accelerations and angular velocities of body movement, is collected via the on-body inertial measurement unit (IMU) sensor. The performance of the air-pressure sensor is verified by comparing with the electromyography and the IMU sensors. To implement our device, we collect the labeled activities data from eight subjects as they perform 11 daily activities. Some commonly used features from raw data are calculated, and five popular classification techniques are evaluated in terms of the accuracy, recall, precision, and F-measure. The experimental results indicate that the proposed wearable device can improve the performance of HAR system. Particularly, the usage of air-pressure sensor can eliminate the confusions among dynamic activities, such as walking and going upstairs, which is an open problem in HAR.

[1]  Kamiar Aminian,et al.  Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease , 2007, IEEE Transactions on Biomedical Engineering.

[2]  Jian Huang,et al.  Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot , 2018, IEEE Access.

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

[4]  B. Bloem,et al.  Quantitative wearable sensors for objective assessment of Parkinson's disease , 2013, Movement disorders : official journal of the Movement Disorder Society.

[5]  Yuqing Chen,et al.  A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[6]  Kok Kiong Tan,et al.  Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living , 2015, IEEE Sensors Journal.

[7]  Shyamsundar Rajaram,et al.  Human Activity Recognition Using Multidimensional Indexing , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Roozbeh Jafari,et al.  A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors , 2016, IEEE Journal of Biomedical and Health Informatics.

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

[10]  Faicel Chamroukhi,et al.  Physical Human Activity Recognition Using Wearable Sensors , 2015, Sensors.

[11]  Mahesh K. Marina,et al.  Towards multimodal deep learning for activity recognition on mobile devices , 2016, UbiComp Adjunct.

[12]  Marco Luca Sbodio,et al.  A Wearable Computing Prototype for supporting training activities in Automotive Production , 2007 .

[13]  Qiang Yang,et al.  Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..

[14]  Haibo Hu,et al.  Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform , 2016, Sensors.

[15]  Oscar Cordón,et al.  Body posture recognition by means of a genetic fuzzy finite state machine , 2011, 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

[16]  Kyoungchul Kong,et al.  Fuzzy Control of a New Tendon-Driven Exoskeletal Power Assistive Device , 2005, AIM 2005.

[17]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[18]  Billur Barshan,et al.  Comparative study on classifying human activities with miniature inertial and magnetic sensors , 2010, Pattern Recognit..

[19]  Cassim Ladha,et al.  ClimbAX: skill assessment for climbing enthusiasts , 2013, UbiComp.

[20]  Joyce Ho Interruptions : using activity transitions to trigger proactive messages , 2004 .

[21]  Marko Kos,et al.  A Wearable Device and System for Movement and Biometric Data Acquisition for Sports Applications , 2017, IEEE Access.

[22]  M. Tomizuka,et al.  A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe , 2009, IEEE/ASME Transactions on Mechatronics.

[23]  Tae-Seong Kim,et al.  A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.

[24]  Alberto G. Bonomi,et al.  Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life , 2011, IEEE Transactions on Biomedical Engineering.

[25]  Jochen Schiller,et al.  Next Generation Cooperative Wearables: Generalized Activity Assessment Computed Fully Distributed Within a Wireless Body Area Network , 2017, IEEE Access.

[26]  Jesse Hoey,et al.  Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  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).

[28]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[29]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.

[30]  Erik Scheme,et al.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.

[31]  Angelo M. Sabatini,et al.  A Smartwatch Step Counter for Slow and Intermittent Ambulation , 2017, IEEE Access.

[32]  John Paul Varkey,et al.  Human motion recognition using a wireless sensor-based wearable system , 2012, Personal and Ubiquitous Computing.

[33]  Yu-Liang Hsu,et al.  A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease , 2016, IEEE Access.

[34]  Pyeong-Gook Jung,et al.  A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors , 2015, IEEE Transactions on Industrial Informatics.

[35]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[36]  Yasuhisa Hasegawa,et al.  Fall Detection and Prevention Control Using Walking-Aid Cane Robot , 2016, IEEE/ASME Transactions on Mechatronics.

[37]  Paola Pierleoni,et al.  A High Reliability Wearable Device for Elderly Fall Detection , 2015, IEEE Sensors Journal.

[38]  Mi Zhang,et al.  A feature selection-based framework for human activity recognition using wearable multimodal sensors , 2011, BODYNETS.

[39]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[40]  Jian Huang,et al.  Posture estimation and human support using wearable sensors and walking-aid robot , 2015, Robotics Auton. Syst..

[41]  K. Kerr,et al.  Analysis of the sit-stand-sit movement cycle in normal subjects. , 1997, Clinical biomechanics.

[42]  Jake K. Aggarwal,et al.  Human activity recognition from 3D data: A review , 2014, Pattern Recognit. Lett..

[43]  Guang-Zhong Yang,et al.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[44]  Angelo Cappello,et al.  Quantitative Description of the Lie-to-Sit-to-Stand-to-Walk Transfer by a Single Body-Fixed Sensor , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[45]  Didier Stricker,et al.  Aerobic activity monitoring: towards a long-term approach , 2014, Universal Access in the Information Society.

[46]  Serena Yeung,et al.  Predicting Mode of Transport from iPhone Accelerometer Data , 2012 .

[47]  Yu-Liang Hsu,et al.  Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network , 2018, IEEE Access.

[48]  Oluwarotimi Williams Samuel,et al.  Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors , 2017, Journal of Medical Systems.

[49]  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.

[50]  Jian Huang,et al.  An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization , 2016, Sensors.