Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

Abstract

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

“…The approach described here has the advantage of negating the requirement of training a system to learn response patterns, as is the case in most conventional pattern recognition systems. The achieved accuracies can be considered favourable given the recognition was performed on activities performed in an out-of-laboratory, semi-naturalistic scenario [34]. The proposed algorithm has low computational complexity, mostly involving comparisons, additions, subtractions and few multiplications.…”

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

“…For the online systems (which are the focus of our approach), three of them get a high accuracy (over 94%) but have some disadvantages. Ermes [49] only applied a subject-dependent evaluation. Besides, their data were collected from only three subjects, which inhibits flexibility to support new users.…”

Activity recognition in smart homes with self verification of assignments

“…ANN is also compared with auto generated and domain knowledge based DTs for activity classification, where auto generated DT shows better accuracy while ANN suffers from over fitting [23]. DT is then combined with ANN in a hybrid classifier model for activity recognition, which merges the prior knowledge of activities with the non-linear classification properties of ANN [24]. Probabilistic Neural Network (PNN) and Fuzzy Clustering based incremental learning method can also be applied for activity recognition [25].…”