//Logo Image
「世大智科/天才家居」-我們創業囉
PDF Version

作者:劉育瑋 (2014-07-17);推薦:徐業良(2015-12-11)

附註:本文為103學年度元智大學機械工程研究所劉育瑋博士論文「結合軟質活動感知床墊建立以床為核心之遠距居家照護系統」第三章。

Chapter 3. The bed-centered telehealth system for home applications

This chapter describes the Bed-Centered Telehealth System (BCTS) information structure, technical details for various application scenarios in the home environment.

3.1    Information structure of the BCTS in home applications

Figure 3-1 shows the information structure of the BCTS for home application scenarios. WhizPAD is put on the bed of the older adult in the home environment. The bedside data processor (BDP) is plugged directly into a home router for Internet connection. The BDP provides the capabilities of DHCP and port forwarding for automatic Internet connection. No special setup of the BDP is required. Remote caregivers can access the BDP via the Internet to browse real-time and historical data from the WhizPAD App on their mobile devices.

Figure 3-1. Information structure of the BCTS in home applications

Dynamic IPs are often used in the home environment. As shown in Figure 3-1, the BDP is scheduled to report its present IP address to a pairing system periodically. After registering into the pairing system, the WhizPAD App obtains the current IP address of the BDP from the pairing system when requesting data, as shown in Figure 3-2. By typing a series of IP address and the specific string of WhizPAD on the web page, the result of real-time monitoring data and historical record from WhizPAD will be displayed on the web page. Figure 3-3 shows the result of real-time monitoring data of WhizPAD displayed on the web page.

Figure 3-2. The display interface of pairing system

Figure 3-3. The real-time data from WhizPAD displayed on the web page

Figure 3-4 shows the user interface of the WhizPAD App, which can be downloaded from online App stores such as Google Play. For real-time sleep monitoring, the WhizPAD App displays on/off bed status, sleep posture, number of movements in bed in the past one minute, and the time of the last movement in bed. The user can also browse historical data from the WhizPAD App in either graphical or text format. In addition to the function of browsing data, it also facilitates alert functions of abnormal events, including reminders of on/off bed and low activity in bed. According to the parameters set up by the user (monitoring period and frequency), the WhizPAD App connects to the BDP and requests data automatically and continuously. If an abnormal event is detected, the App will alert and pop the reminder message to the user, as shown in Figure 3-5.

Figure 3-4. The display interface of the WhizPAD App

Figure 3-5. The alert of abnormal events

More functions are being developed for the next generation WhizPAD, such as respiration rate detection and sleep quality assessment, and are added into the WhizPAD App. The respiration rate is displayed on the real-time monitoring as shown in Figure 3-6 (a). Figure 3-6 (b) shows the result of sleep quality assessment, which displays the sleep/ awake state. In the meantime, the detail sleep report can also be provided, including time in bed, sleep latency, awake time in bed, sleep efficiency, body movements in bed, etc.

Figure 3-6. Sleep quality assessment

In addition to remote applications, an appliance control module is developed for simple bedside lamp control as an example. The appliance control module integrates with a microchip, a Zigbee module, a relay and a socket power point, as shown in Figure 3-7. According to the detection of on/off bed and activities by WhizPAD, the BDP sends a command signal to the appliance control module for power control. When WhizPAD detects the event of on bed and continuous low activities, the lamp connecting to the socket power point will be turned off; as the user leaves the bed, the lamp will be turned on immediately.

IMAG1114

Figure 3-7. Appliance control module of BTCS

3.2    Interpretation of physical activities in bed of home users

The life of the older adults is usually simple and regular. Based on the tele-monitoring of long-term physical activities in bed, the BCTS can help remote family members understand the life pattern and sleep situation of the older adults in the home environment. For interpreting the long-term physical activities in bed of older adults at home, five older adults with motor capability, one male, four females, aged 54-90 years old, weighing 42-73 kg were recruited. Continuous data of one week of physical activities in bed was collected with the consent of the home subjects. In this investigation, three bed-related indices were used to interpret the long term physical activities in bed of older adults, including the average number of body movements in bed per minute (/minute), percentage of in-bed time in a day (%) and off-bed times in a day (/day).

Table 3-1 shows the analysis result. Figure 3-8 to Figure 3-12 shows the one-week data of the five older adults. Case 1 is 90 years old, the oldest among the five older adults. She also has the longest in-bed time (57% in a day) among the 5 older adults. Case 2 has the lowest average number of movements in bed (0.55), which may imply that she has better sleep quality. Case 3 is a 57-year old male who has an irregular working schedule. The standard deviation of in-bed time of Case 3 is also the highest (9.74%) among the 5 older adults. Case 3 exercises on the bed before getting off the bed, so the average number of body movements in bed per minute is also the highest (1.79). Case 4 and Case 5 are two females who have regular working schedules. This can be reflected in the low standard deviation of in-bed time (4.00% and 3.78%).

Table 3-1. Three bed-related indices for the five older adults

Case

Bed-related indices

D1

D2

D3

D4

D5

D6

D7

Average/SD

1 (F, 90Yrs)

Movements in bed

1.21

1.14

1.29

1.74

1.12

1.21

1.31

1.29/0.21

Time in bed (%)

53.77

66.58

61.83

61.83

48.59

56.28

48.36

56.75/7.01

Off-bed per day

2

2

4

4

4

5

2

3.28/1.25

2 (F, 77Yrs)

Movements in bed

0.39

0.48

0.69

0.51

0.65

0.46

0.65

0.55/0.11

Time in bed (%)

45.84

41.67

43.81

41.82

44.99

37.21

46.88

43.17/3.28

Off-bed per day

5

5

4

3

4

2

3

3.71/1.11

3 (M, 57Yrs)

Movements in bed

1.43

1.96

1.94

1.74

2.09

1.64

1.77

1.79/0.22

Time in bed (%)

34.72

32.64

29.17

48.96

36.46

53.47

50.00

40.78/9.74

Off-bed per day

2

2

2

2

3

4

3

2.57/0.79

4 (F, 54Yrs)

Movements in bed

1.06

1.47

1.00

1.20

1.06

1.66

1.52

1.28/0.26

Time in bed (%)

37.54

33.96

29.78

31.05

28.82

38.54

37.08

33.82/4.00

Off-bed per day

1

1

1

1

1

2

2

1.29/0.49

5 (F, 56Yrs)

Movements in bed

1.75

1.29

1.02

1.84

1.12

1.46

1.09

1.37/0.33

Time in bed (%)

46.52

37.61

39.95

39.32

45.54

38.62

37.23

40.69/3.78

Off-bed per day

1

1

1

1

1

1

1

1.00/0.00

Figure 3-8. The long term data of a normal healthy older adult who takes naps and lies in bed almost half day (Case 1)

Figure 3-9. The long term data of a normal healthy older adult who has the lowest average number of movements in bed (Case 2)

Figure 3-10. The long term data of a normal healthy older adult who has an irregular working schedule (Case 3)

Figure 3-11. The long term data of a normal healthy older adult who has regular working schedule (Case 4)

Figure 3-12. The long term data of a normal healthy older adult who has regular working schedule (Case 5)

3.3    Long-term in-bed patterns of home users

General in-bed patterns can also be observed on a long-term basis. In this study, the average profile of in-bed status for the one-week period is used to explore the rhythm of life pattern of the older adults. Figure 3-13 compares the average profile of in bed status of Case 1, 3 and 5. Three different in-bed patterns can be found. Case 1 has the higest in-bed time (56.75%). She takes naps in the afternoon. Case 3 has an irregular in-bed pattern with the highest standard deviation (0.08). Due to an irregular working schedule, he goes to bed at around 10 pm when not on duty and at 2 am when on duty. Case 5 has a regular in-bed pattern with the lowest standard deviation (0.02).

Figure 3-13. Average profiles of in bed status for Case1 (a), Case3 (b), and Case 5 (c)

By calculating the correlation between the monitoring data of a given day with the average in-bed status profile, the correlation coefficient can be used as an indication of whether the one-day monitoring data is close to the long-term profile. Figure 3-14 shows examples of correlation coefficients between the one-day monitoring data and average profiles of Case 1, 3 and 5. The correlation coefficients of in-bed status are 0.74, 0.85, and 0.95, respectively.

 

Figure 3-14. Examples of correlation coefficient between the one-day monitoring data and average profiles (a). Case1 (b). Case3 (c). Case 5