Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case Study

Monitoring indoor activities of daily living (ADLs) of a person is neither an easy nor an accurate process. It is subjected to dependency on sensor type, power supply stability, and connectivity stability without mentioning artifacts introduced by the person himself. Multiple challenges have to be overcome in this field, such as; monitoring the precise spatial location of the person, and estimating vital signs like an individuals average temperature. Privacy is another domain of the problem to be thought of with care. Identifying the persons posture without a camera is another challenge. Posture identification assists in the persons fall detection. Thermal imaging could be a proper solution for most of the mentioned challenges. It provides monitoring both the persons average temperature and spatial location while maintaining privacy. In this research, we propose an IoT system for monitoring an indoor ADL using thermal sensor array (TSA). Three classes of ADLs are introduced, which are daily activity, sleeping activity and no-activity respectively. Estimating person average temperature using TSAs is introduced as well in this paper. Results have shown that the three activity classes can be identified as well as the persons average temperature during day and night. The persons spatial location can be determined while his/her privacy is maintained as well.

A comparison of three heart rate detection algorithms over ballistocardiogram signals

Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging for various reasons. For example, BCG signals' morphology can vary between and within-subjects. Also, it differs from one sensor to another. Hence, it is essential to evaluate HR detection algorithms across different datasets and under different experimental setups. This paper investigated the suitability of three algorithms (i.e., MODWT-MRA, CWT, and template matching) for HR detection across three independent BCG datasets. The first two datasets (Datset1 and DataSet2) were obtained using a microbend fiber optic (MFOS) sensor, while the last one (DataSet3) was obtained using a fiber Bragg grating (FBG) sensor. DataSet1 was collected from 10 OSA patients during an in-lab PSG study, Datset2 was obtained from 50 subjects in a sitting position, and DataSet3 was gathered from 10 subjects in a sleeping position. The CWT with derivative of Gaussian (Gaus2) provided superior results than the MODWT-MAR, CWT (frequency B-spline-Fbsp-2-1-1), and CWT (Shannon-Shan1.5-1.0) for DataSet1 and DataSet2. That said, a BCG template was constructed from DataSet1. Then, it was applied for HR detection across DataSet2. The template matching method achieved slightly superior results than CWT-Gaus2 for DataSet2. Furthermore, it has proved useful for HR detection across DataSet3 despite that BCG signals were obtained from a different sensor and under different conditions. Overall, the time required to analyze a 30-second BCG signal was in a millisecond resolution for the three proposed methods. The MODWT-MRA had the highest performance, with an average time of 4.92 ms.