IoT in medical diagnosis: detecting excretory functional disorders for Older adults via bathroom activity change using unobtrusive IoT technology

Abstract

The Internet of Things (IoT) and Artificial Intelligence (AI) are promising technologies that can help make the health system more efficient, which concurrently can be particularly useful to help maintain a high quality of life for older adults, especially in light of healthcare staff shortage. Many health issues are challenging to manage both by healthcare staff and policymakers. They have a negative impact on older adults and their families and are an economic burden to societies around the world. This situation is particularly critical for older adults, a population highly vulnerable to diseases that needs more consideration and care. It is, therefore, crucial to improve diagnostic and management as well as proposed prevention strategies to enhance the health and quality of life of older adults. In this study, we focus on detecting symptoms in early stages of diseases to prevent the deterioration of older adults' health and avoid complications. We focus on digestive and urinary system disorders [mainly the Urinary Tract Infection (UTI) and the Irritable Bowel Syndrome (IBS)] that are known to affect older adult populations and that are detrimental to their health and quality of life. Our proposed approach relies on unobtrusive IoT and change point detections algorithms to help follow older adults' health status daily. The approach monitors long-term behavior changes and detects possible changes in older adults' behavior suggesting early onsets or symptoms of IBS and UTI. We validated our approach with medical staff reports and IoT data collected in the residence of 16 different older adults during periods ranging from several months to a few years. Results are showing that our proposed approach can detect changes associated to symptoms of UTI and IBS, which were confirmed with observations and testimonies from the medical staff.

Keywords: Internet of Things; early detection; excretory functional disorders; irritable bowel syndrome; older adults; urinary tract infection.

Figure 1

Schematic description of the bathroom with the spatial location of sensors.

Figure 2

Sensor signal values for a snapshot of activity.

Figure 3

Bathroom activity time series for subject A, with detected change time stamps overlapped as vertical lines.

Figure 4

Bathroom activity time series for subject B, with detected change time stamps overlapped as vertical lines.

Figure 5

Bathroom activity time series for subject C, with detected change time stamps overlapped as vertical lines.

Figure 6

Bathroom activity time series for subject D, with detected change time stamps overlapped as vertical lines.

Figure 7

Bathroom activity time series for subject E, with detected change time stamps overlapped as vertical lines.

Figure 8

Bathroom activity time series for subject F, with detected change time stamps overlapped as vertical lines.

Figure 9

Bathroom activity time series for subject G, with detected change time stamps overlapped as vertical lines.

Figure 10

Bathroom activity heatmap for subject A grouped by monthly periods.

Figure 11

Bathroom activity heatmap for subject B grouped by monthly periods.

Figure 12

Bathroom activity heatmap for subject C grouped by monthly periods.

Figure 13

Bathroom activity heatmap for subject D grouped by monthly periods.

Figure 14

Bathroom activity heatmap for subject E grouped by monthly periods.

Figure 15

Bathroom activity heatmap for subject F grouped by monthly periods.