Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

Abstract

A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 ± 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established.

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

“…In CBGM, noise removal approaches include adaptive iterative filtering and fast discrete lifting-based wavelet transform (LWT) [26] as well as multi-filtering augmentation [27]. Pulse oximetry sensor data often utilize adaptive filtering techniques [28], while accelerometer and gyroscope sensors benefit from Butterworth high-pass filtering [29], complementary filters, and Kalman filters [30] for error assessment and enhanced accuracy. Artifacts can be effectively removed from EEG sensors using graph signal processing [31].…”

“…Among the most widely used ML algorithms for overcoming accuracy limitations in body sensors are Bayesian models such as Gaussian naive Bayes (NB), probabilistic approaches, and nonparametric methods such as exemplar-based techniques, kernel methods, decision trees (DT), random forests (RF), bagging, and boosting. Other commonly employed algorithms are logistic regression (LoR), linear regression (LR), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) [29,[35][36][37][38][39]. Additionally, advanced signal processing techniques such as ANOVA, chi-square, mutual information, and ReliefF are utilized to enhance accuracy [40].…”