Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

Abstract

Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy (94.66%) was achieved by combining both types of sensors.

A Rapid Detection of Parkinson’s Disease using Smart Insoles: A Statistical and Machine Learning Approach

“…Set the time window for this study to 5 seconds, we chose to use statistical features which, according to our hypothesis, can reach the same level of reliability as gait parameters. The statistical features are widely used for the analysis of data coming from the lower limbs and have allowed obtaining high performance in related research such as in human activity recognition applications [27]. Two kinds of features have been extracted, time-domain features and frequency-domain features.…”

Human Activity Recognition Using Inertial Sensors in a Smartphone: Technical Background(Review)

“…Consequently, the precision of outcomes in human activity recognition systems is heavily contingent on the technological advancements in sensors and the accuracy and fidelity of signals they record. This underscores the fact that progress in sensor and accelerator technologies directly influences human activity recognition systems' classification and recognition capabilities [16].…”

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

“…However, these characteristics were heuristically chosen, which could lead to poor results when analysing new data. Feature selection techniques can be used to reduce irrelevant features [26], but they still use the initially determined set of features. As a result, algorithms that allow the processing of raw data, such as deep learning models, have become increasingly popular in recent years.…”