Evidence-based multi-sensor information fusion for remote health care systems

• This study designs a remote Android platform IOT home community health care wireless network.
• Physiological signal processing fusion algorithms and the theory of evidence are used.
• To improve fusion result accuracy and reduce the measurement time due to different classification error signals.
• Physiological behavior for medical personnel assisted diagnosis.

This study designs a remote Android platform IOT home community health care wireless network. This is an interdisciplinary study of Android platform IOT home community health care system implementation. This system maintains patient identity, signal processing and results collection and analysis via cloud computing. Results are transmitted to the medical host for diagnosis. The target patient population is family geriatric psychiatric patient communities and remote monitoring healthcare situation. Physiological signal processing fusion algorithms and the theory of evidence are used in this study to improve fusion result accuracy and reduce the measurement time due to different classification error signals. In addition to emphasizing cloud computing, Android and wireless sensor network technology is used to achieve a variety of physiological signals correctly classified by Instant Screenshot Collection. The physiological signal measured results reflect the patient physiological behavior for medical personnel assisted diagnosis.