Cost-effective Elderly Fall Detection with Symmetry Transformer Networks
Journal Title:
2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
Li, B., Cui, W., Chen, Y., Zhou, J. T., Chen, Z., Li, Y., & Min, W. (2022). Cost-effective Elderly Fall Detection with Symmetry Transformer Networks. 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). https://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00277
Abstract:
Elderly fall detection aims at automatically detecting fall actions, a major public health problem for the elderly. Existing fall detection methods are cost-ineffective - a satisfactory performance is commonly traded by high costs in device access/deployment and positive record acquirement. In this paper, we set out to devise a WiFi-based elderly fall detection approach in a cost-effective manner - being high in detection accuracy, and low in costs of device access and positively -annotated data. Specifically, our system builds on commercial WiFi, a ubiquitously available device, which greatly saves device access and deployment costs. To extract the nuanced and implicit features due to the low signal-to-noise ratio of WiFi signals, we propose symmetry Transformer networks, a variant of Transformer to facilitate better feature representation learning. Meanwhile, to overcome the positive scarcity and low inter-environment transferability brought by Transformer, we propose a novel two-stage training scheme, where the representation learning is performed in an unsupervised manner. This unveils a transferability property that reduces the requirements on positive instances in training the resulting model. Empirical evaluation demonstrates our cost-effective desideratum while achieving superior performance compared with state-of-the-art models.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done