Multisensors and the real‐time hybrid Wi‐SUN/Wi‐Fi transmission system

The new ICT multisensor (IMS)‐ambulatory BP monitoring (ABPM) system uses new ICT/IoT‐based biological and environmental signal monitoring that can simultaneously monitor the environment (temperature, illumination, and humidity) at five different locations in a house (the entryway, bedroom, bathroom, toilet, and living room) and activities measured by using wrist‐type high‐sensitive actigraph to identify the location of patients (Figure 7.8) [172] . Using cloud computing with an IoT gateway and bridges based on hybrid Wi‐SUN, 92 LTE, and Bluetooth Low Energy (BLE) transmission systems, we can collect and store individual time‐series home and ambulatory BP data, waveform data, and data on individual physical activity and environmental signals specific to different rooms in the house (Figure 7.9) [172] . The IoT bridges collect the data from the IMS‐ABPM, wrist‐type actigraph, and body weight scale via BLE transmission. Data are then converted to Wi‐SUN transmission signal format and transmitted to the IoT gateway by a Wi‐SUN multihop transmission system. The Wi‐SUN system is based on IEEE 802.15.4g‐based international new IoT standards 92 and certified by the Wi‐SUN alliance. In the IoT gateway, the received Wi‐SUN signal is also converted to LTE transmission signal format and transmitted to the cloud over a conventional LTE wide‐area network. Moreover, each IoT bridge has devices for environmental monitoring such as a thermometer, illuminometer, and hygroscope. The environment‐monitoring data at each IoT bridge is also transmitted to the cloud.

Housing conditions are important for the prevention of cardiovascular disease events, the onset of which exhibits significant seasonal variation with a peak in the winter. The death rate, especially the cardiovascular death, increases in the winter. On the other hand, data show that the winter mortality rate was higher in the north Kanto area (middle of Japan) than in Hokkaido, where winters were severely cold [Figure 2.30]. This inverse phenomenon is considered to be partly due to the increasing prevalence of energy‐saving homes with good thermal insulation performance in colder countries. Our hypothesis is that heterogeneity of room temperatures within the home (i.e. different temperatures between rooms, and even within the same room) could increase the risk of cardiovascular disease events by contributing to exaggerated BP variability.

AI and anticipation models

In the Anticipate study using IMS‐ABPM, mixed‐model analysis of the association between daytime ambulatory systolic blood pressure (SBP) and physical activity, temperature, atmospheric pressure, and season (4699 data points obtained from 79 patients in summer and 72 patients in winter) demonstrated that a 10°C decrease in temperature was associated with a 10.4 mmHg increase in SBP (Figure 7.10) [172] . For example, SBP of 130 mmHg at rest (100 G) in an indoor setting at 25°C will increase to 151 mmHg when the conditions change to walking (1000 G) outdoors at 5°C (Figure 7.10).

In a prediction model of hypertension, AI increases the prediction accuracy for future‐onset hypertension in normotensive patients [477] . Age, body mass index, the cardio‐ankle vascular index (CAVI), and triglycerides were selected in this algorithm. In the recent Predict study using time series of home BP data and room temperature, AI allowed the prediction of BP levels over the coming two weeks to within approximately 6 mmHg [478] (Figures 7.11 and 7.12).

Development of wearable beat‐by‐beat (surge) BP monitoring

The current status and future direction of out‐of‐office BP monitoring are summarized in Figure 7.13. In future, wearable 24‐hour BP monitoring/guided step 24‐hour BP monitoring could facilitate the achievement of perfect 24‐hour BP management (Figure 7.14). The research and development of continuous beat‐by‐beat “surge” BP monitoring has started, but it is still in the early stages [41, 172].

There are marked individual differences in the short‐term dynamic BP change caused by various triggers (Figure 7.15) [479] . Wearable noninvasive beat‐by‐beat BP monitoring has been the dream of doctors who manage hypertension. Omron Healthcare Co., Ltd. recently publicized the prototype of a wearable surge BP monitoring (WSP) that uses recent advances in automatically controlled technology and measures absolute values of the maximum peaks of beat‐by‐beat pressure (Figure 7.16). The first prototype (WSP‐1) has two tonometry sensor plates and the angle of the arrayed sensor plate to cover the radial artery is automatically adjusted to obtain effective applanation. This device is being tested and improved in collaboration with Omron, with the goal of developing more accurate beat‐by‐beat WSP (Figure 7.16) [156] .

Using the WSP device, continuous beat‐by‐beat BP during sleep was monitored simultaneously with polysomnography (Figure 7.17). Nighttime BP and BP variability were significantly lower during Stages 2 and 3 sleep, and higher in Stage 1 and rapid eye movement (REM) sleep, and during waking hours (Figure 7.18) [235] . Three nighttime BP surges were detected, associated with REM sleep, arousal (unconscious microarousal) (Figure 7.19a), and a sleep apnea episode (Figure 7.19b) [235] .