Anomaly Detection in Real Scarce Data: A Case Study on Monitoring Elderly's Physical Activity and Sleep

There is a plethora of work in the literature on anomaly detection with statistical or machine learning-based approaches. Most often there are several problems and pitfalls that anomaly detection must overcome to be successful in practice. While the majority of the work is validated on general benchmarks including a lot of synthetic datasets, unfortunately there are only a few studies on real data in certain application domains. This paper aims to fill this gap in the field of sleep and physical activity monitoring of the elderly. More precisely, it provides a case study of anomaly detection in the context of monitoring the activities of elderly people. Our objective is to highlight the main problems that can be encountered in such a real context and the solutions that can be provided. Hence, we raised several questions and provided practical answers, particularly with regard to data scarcity, heterogeneity, class imbalance and lack of ground truth.