Human activity detection using machine learning methods from wearable sensors

• Published in: Sensor review Vol. 40; no. 5; pp. 591 - 603
• Main Authors: Randhawa, Princy, Shanthagiri, Vijay, Kumar, Ajay, Yadav, Vinod
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 02.10.2020; Emerald Group Publishing Limited
• Subjects: Accelerometers; Algorithms; Classification; Dance; Design; Human activity recognition; Human motion; Machine learning; Pressure sensors; Sensors; Smartphones; Support vector machines; Walking; Wearable technology; Fabric sensors; Activity recognition; Classifier; Decision tree; SVM classifier; Wearable sensors; Women safety
• ISSN: 0260-2288; 1758-6828
• DOI: 10.1108/SR-02-2020-0027

Purpose The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity. Design/methodology/approach The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification. Findings The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms. Practical implications This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built. Originality/value Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.