Abstract
Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.
Highlights
Wearable internet of things (IoT) devices have the potential to change the landscape in health and activity monitoring [1,2]
We provide a customizable framework for human activity recognition (HAR), where users have the freedom to insert their algorithms at any step of the framework, including segmentation, feature selection, and classification
We provide a brief description of the dataset and classification accuracy on each dataset
Summary
Wearable internet of things (IoT) devices have the potential to change the landscape in health and activity monitoring [1,2]. They are already employed for in-home monitoring of movement disorders to provide doctors better insight into their patients’ daily activities [3]. These applications enable automatic tracking of the activities of users, such as walking, which can provide valuable insight to both users and health specialists, since self-recording is inconvenient and unreliable. Human activity recognition (HAR) using low-power wearable devices can revolutionize health and activity monitoring applications. There is a critical need for open-source datasets that provide a common platform for HAR research
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.