A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

Abstract

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

Changes in Physical Activity among United Kingdom University Students Following the Implementation of Coronavirus Lockdown Measures

“…Powerful and intuitive longitudinal analysis techniques for repeated-measures data are available and commonly used in specialisms such as epidemiology [ 22 , 23 ]. Previous studies often approach the analysis of RMT data using data science approaches [ 20 , 24 , 25 ], whereas an epidemiological analytical approach could yield useful and informative insights. With PA during the COVID-19 pandemic being of significant interest to epidemiologists and data scientists alike, we took this opportunity to leverage methodological techniques from both disciplines, modelling RMT data using epidemiological analysis techniques.…”

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

“…where, in Equations (12)- (14), m is the consecutive vector sequence, n is the gradient, r is the width of the boundary of the exponential function, N is the sample time series, and D m ij is the degree of similarity. Following, we used different values for n and r, which leads to a decrease in the standard deviation.…”

Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

“…Sensors generate a large amount of data in each time step t, creating a requirement to study efficient algorithms for data analysis [ 41 ]. To detect physical activity for healthcare purposes, reference [ 42 ] proposed to use gyroscope and accelerometer sensors. These sensors help to examine and understand daily routine life and different postures.…”

“…However, data generated through these sensors need to be cleaned and transformed. For the task of activity recognition, feature extraction from these sensor data is done using traditional statistical methods in [ 42 ]. Reference [ 43 ] used accelerometer-based sensors attached to the human body to detect various activities for patient monitoring in hospital environments.…”