The Wearables Development Toolkit

Author:

Haladjian Juan1

Affiliation:

1. Technische Universität München, Boltzmanstr., Munich, Germany

Abstract

Although the last two decades have seen an increasing number of activity recognition applications with wearable devices, there is still a lack of tools specifically designed to support their development. The development of activity recognition algorithms for wearable devices is particularly challenging because of the several requirements that have to be met simultaneously (e.g., low energy consumption, small and lightweight, accurate recognition). Activity recognition applications are usually developed in a series of iterations to annotate sensor data and to analyze, develop and assess the performance of a recognition algorithm. This paper presents the Wearables Development Toolkit, an Integrated Development Environment designed to lower the entrance barrier to the development of activity recognition applications with wearables. It specifically focuses on activity recognition using on-body inertial sensors. The toolkit offers a repository of high-level reusable components and a set of tools with functionality to annotate data, to analyze and develop activity recognition algorithms and to assess their recognition and computational performance. We demonstrate the versatility of the toolkit with three applications and describe how we developed it incrementally based on two user studies.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-05-13

2. Evaluation of Video-Assisted Annotation of Human IMU Data Across Expertise, Datasets, and Tools;2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2024-03-11

3. LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control;IEEE Access;2023

4. ODIN AD: A Framework Supporting the Life-Cycle of Time Series Anomaly Detection Applications;Advanced Analytics and Learning on Temporal Data;2023

5. MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data;Sensors;2022-08-05

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