AI-Fairness Towards Activity Recognition of Older Adults
Mohammad Arif Ul Alam
Abstract
Wireless wearable sensor networks (WSN)-based activity recognition has implicit impacts on context-aware application and connected health research for older adults. Although, many existing researches focus on different WSN integration, signal processing and intelligent detection to recognize different activities, none of the existing works address the Artificial Intelligence (AI) fairness in activity recognition of older adults domain. We argue that AI-fairness towards detecting activities of older adults is different than to fairness for other protected attributes such as age, gender or race. The primary reason behind this difference is the diversity of same activity among different older adults based on their functional abilities (walking using crutches, walker). However, the diversity also exists among the same older adults based on time and space as well. The above constraints limit the AI capabilities and causes unfair detection of daily activities that has potential impacts on healthcare interventions for older adults. In this paper, we investigate, first of its kind, AI-fairness of activity recognition for older adults using a single wearable WSN sensor in presence of diverse disabilities. In this regard, we (i) employ signal processing and Bi-directional LSTM model to recognize diverse multi-label activities of older adults using single WSN; (ii) identify the existence of biases in activity recognition considering the age and functional ability as protected attributes; (iii) mitigate the biases by applying different bias mitigation techniques in different stages (pre-, in- and post-processing) of machine learning model development; and finally (iv) we experimentally evaluate the proposed AI-fairness framework using older adults data collected from a retirement center.