Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Abstract

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.

Accepted in ACM Transactions on Computing for Healthcare

References in corpus (34)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Improving neural networks by preventing co-adaptation of feature detectors
• WaveNet: A Generative Model for Raw Audio
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• PaLM: Scaling Language Modeling with Pathways
• Time Series Forecasting With Deep Learning: A Survey
• Towards Personalized Federated Learning
• Applications of Deep Learning and Reinforcement Learning to Biological Data
• Training Compute-Optimal Large Language Models
• A Review of Generalized Zero-Shot Learning Methods
• Self-supervised ECG Representation Learning for Emotion Recognition
• Recent Advances in Autoencoder-Based Representation Learning
• Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
• PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
• Transformers in Time Series: A Survey
• DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
• A Transformer Architecture for Stress Detection from ECG
• Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
• Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation Learning
• Wide-Baseline Relative Camera Pose Estimation with Directional Learning
• Identity and Posture Recognition in Smart Beds with Deep Multitask Learning
• In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection
• Cascaded WLAN-FWA Networking and Computing Architecture for Pervasive In-Home Healthcare
• An Attentive-based Generative Model for Medical Image Synthesis
• Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion
• Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators
• Multimodal Estimation of End Point Force During Quasi-dynamic and Dynamic Muscle Contractions Using Deep Learning
• When Healthcare Meets Off-the-Shelf WiFi: A Non-Wearable and Low-Costs Approach for In-Home Monitoring
• An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
• Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
• Real-time privacy preserving disease diagnosis using ECG signal
• On The Relationship Between Continual Learning and Long-Tailed Recognition
• Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data