Multimodal Systems for Independent Living

Md Zia Uddin

Abstract: The advancement of multimodal sensing technologies has significantly enhanced independent living, particularly for older adults and individuals with disabilities. This chapter explores diverse sensor modalities used in smart home environments to monitor daily activities, detect anomalies, and ensure safety. Key categories include wearable devices, ambient sensors, camera systems, acoustic methods, pressure and floor sensors, radar and RF-based approaches, and smart objects. Wearable sensors, such as accelerometers, gyroscopes, and physiological monitors, offer real-time data for tracking movement, posture, heart rate, and respiration, supporting applications like fall detection and health monitoring, though they face challenges like limited battery life, data reliability, and user adherence. Ambient sensors—motion detectors, door/cabinet sensors, and smart appliances—enable passive monitoring of activity patterns and risks such as inactivity or unusual usage. Camera-based systems leverage computer vision, depth imaging, and techniques like skeleton tracking and convolutional neural networks (CNNs) for activity recognition, though privacy concerns demand solutions such as edge processing and anonymized data extraction. Acoustic sensing employs audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), spectrograms, and beamforming to detect distress signals and anomalies. Pressure sensors embedded in furniture and floors analyze weight distribution, occupancy, and gait stability, complementing other sensing approaches. Radar and RF-based solutions, including Wi-Fi Channel State Information (CSI), provide contactless monitoring of motion and vital signs, making them suitable for privacy-sensitive environments, although signal interference and environmental factors can affect performance. Sensor-augmented objects like smart canes and pill dispensers enable tracking of mobility, medication adherence, and daily habits without relying on wearables. The integration of heterogeneous data sources remains a key challenge, with methods such as hierarchical fusion, temporal alignment, and contextual correlation improving detection accuracy and reducing false positives. Longitudinal data analysis facilitates early identification of health trends such as mobility decline or cognitive impairment. Ethical considerations—including data privacy, transparent decision-making, and adaptability to user lifestyles—are critical for acceptance and success. By combining advanced sensing with machine learning-based analysis, these systems offer scalable, adaptive, and privacy-conscious solutions to support independent living and improve the quality of life for vulnerable populations.

External IDs:doi:10.1007/978-3-031-97359-8_2