Human Health Activity Intelligence Based on mmWave Sensing and Attention Learning

Yichen Gao, Noah Ziems, Shaoen Wu, Honggang Wang, Mahmoud Daneshmand

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Human daily activity monitoring has its particular significance in smart health. Human activity recognition based on mmWave has drawn enormous research efforts and achieved significant progress. Most of these solutions, however, work on data that has been manually segmented for each piece to contain only a single activity, which is impractical in reality where the sensor continuously generates data containing a series of activities. To address this challenge, this paper proposes a multi-head attention model that can detect the transition from one activity to another in a stream of mmWave sensor data of various human activities by analyzing the inner correlation of mmWave radar data fragments with a sliding window mechanism. Furthermore, the model then recognizes the new activity type in the data once it detects an activity transition. The solution has been extensively evaluated with a sparse point cloud dataset generated by a mmWave radar, which contains five types of activities. The experiment results show that the solution can achieve an accuracy of 98% in detecting activity transition at its best.

Original languageEnglish
Pages (from-to)1391-1396
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

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