Scholarly article on topic 'Development of a bed-centered telehealth system based on a motion-sensing mattress'

Development of a bed-centered telehealth system based on a motion-sensing mattress Academic research paper on "Medical engineering"

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Abstract of research paper on Medical engineering, author of scientific article — Yu-Wei Liu, Yeh-Liang Hsu, Wei-Yi Chang

Abstract Purpose Given the rapid increase in the aging population and the decline in birth rate, there is a growing demand for telehealth services. For older adults who are living at home or in nursing homes, the bed is an integral part of their daily lives. Telemonitoring of physical activities in bed can provide valuable information of the status of an older adult. This paper presents a Bed-Centered Telehealth System (BCTS), which uses the bed as the center of health data collection for telehealth systems implemented in homes and nursing homes. Methods The core sensor of the BCTS is a soft motion-sensing mattress, WhizPAD, which collects signals of physical activities in bed that can be classified into events such as on/off bed, sleep posture, movement counts, and respiration rate. Results Integrated with information and communication systems, caregivers can maintain awareness of older adults' daily activities and needs by using their mobile devices to access the BCTS for real-time monitoring and historical data record of bed-related activities, as well as receiving service reminders and alerts for abnormal events. Scenarios of using the BCTS in the homes and nursing homes are described. Conclusion The design concept of BCTS is to integrate telehealth functions into something that already exists in the home, namely the bed. Future extensions of the BCTS to include other telemonitoring functions are discussed.

Academic research paper on topic "Development of a bed-centered telehealth system based on a motion-sensing mattress"

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Journal of Clinical Gerontology & Geriatrics

journal homepage: www.e-jcgg.com

Original article

Development of a bed-centered telehealth system based on a motion-sensing mattress

Yu-Wei Liu, PhD a'b, Yeh-Liang Hsu, PhD a'b' *, Wei-Yi Chang, BS a'b

CrossMark

a Gerontechnology Research Center, Yuan Ze University, Taoyuan, Taiwan b Mechanical Engineering Department, Yuan Ze University Taoyuan, Taiwan

ARTICLE INFO

Article history: Received 1 April 2014 Received in revised form 3 June 2014 Accepted 16 June 2014 Available online 15 August 2014

Keywords: motion-sensing older adults sleep monitoring telehealth system

ABSTRACT

Purpose: Given the rapid increase in the aging population and the decline in birth rate, there is a growing demand for telehealth services. For older adults who are living at home or in nursing homes, the bed is an integral part of their daily lives. Telemonitoring of physical activities in bed can provide valuable information of the status of an older adult. This paper presents a Bed-Centered Telehealth System (BCTS), which uses the bed as the center of health data collection for telehealth systems implemented in homes and nursing homes.

Methods: The core sensor of the BCTS is a soft motion-sensing mattress, WhizPAD, which collects signals of physical activities in bed that can be classified into events such as on/off bed, sleep posture, movement counts, and respiration rate.

Results: Integrated with information and communication systems, caregivers can maintain awareness of older adults' daily activities and needs by using their mobile devices to access the BCTS for real-time monitoring and historical data record of bed-related activities, as well as receiving service reminders and alerts for abnormal events. Scenarios of using the BCTS in the homes and nursing homes are described. Conclusion: The design concept of BCTS is to integrate telehealth functions into something that already exists in the home, namely the bed. Future extensions of the BCTS to include other telemonitoring functions are discussed.

Copyright © 2014, Asia Pacific League of Clinical Gerontology & Geriatrics. Published by Elsevier Taiwan

LLC. All rights reserved.

1. Introduction

Given the rapid increase in the aging population and the decline in birth rate, there is a growing demand for telehealth services. For older adults who are living at home or in nursing homes, the bed is an integral part of their daily lives. They often spend a long time lying in bed at home for rest and sleep. In nursing homes, the bed is often used as a unit for care service management. Therefore, tele-monitoring of activities in bed provides valuable information of the status of an older adult.

Many care systems have been developed based on activities detected in bed, for example, detection of bed-exit and fall even-ts,1-3 recognition of sleep pattern and quality,4-7 prediction of early signs of illness in older adults,8 and the monitoring of obstructive

* Corresponding author. Department of Mechanical Engineering, Yuan Ze University, Number 135, Yuan-Tung Road, Chung-Li City, Taoyuan County, 32003, Taiwan.

E-mail addresses: mehsu@saturn.yzu.edu.tw, s988703@mail.yzu.edu.tw (Y.-L Hsu).

sleep apnea syndrome.9 In such systems, motion sensing in bed, or "bed actigraphy", is often the core technique.

Bed actigraphy is defined as the measurement of movement in bed. Various types of noninvasive and unrestrained sensing techniques have been implemented for this purpose. Load cells or force sensors are the most common sensing components used to detect body movements in bed. Nishida et al10 presented the idea of a robotic bed, which is equipped with 221 pressure sensors for monitoring respiration and body position. Van Der Loos et al11 proposed a system called SleepSmart, composed of a mattress pad with 54 force-sensitive resistors and 54 resistive temperature devices, to estimate body center of mass and index of restlessness. Many pad-based solutions have been proposed. Erkinjuntti et al12 presented a design of the static charge-sensitive bed for long-term monitoring of respiration, heart rate, and body movements, which Kaartinen et al13 used to determine the relation between movements in bed and sleep quality. Watanabe et al4 designed a pneumatic-based system for sleep monitoring. A thin, air-sealed cushion is placed under the bed mattress of the user, and the small movements attributable to human automatic vital functions

http://dx.doi.Org/10.1016/j.jcgg.2014.06.001

2210-8335/Copyright © 2014, Asia Pacific League of Clinical Gerontology & Geriatrics. Published by Elsevier Taiwan LLC. All rights reserved.

are measured as changes in pressure using a pressure sensor. Optical systems for monitoring bed-related activities have also been developed. Aoki et al14 proposed a nonrestrictive and noncontact respiratory movement monitoring system utilizing a three-dimensional vision sensor to monitor respiratory movement in sleep. These systems implemented sensors into the bed, an approach whose complexity and cost may limit their practical use.

Textile-based sensing techniques have been developed to provide unobtrusive monitoring of vital parameters and activities. Cheng et al6 proposed a portable device to evaluate body movements with quantitative measurement and to recognize sleep pattern and quality. Carvalho et al15 developed textile and polymer applications (cushions, mattresses, and mattresses overlays) able to monitor and control the pressure in the body's areas that are in contact with the support surfaces. Peltokangas et al16 proposed an integrated system that uses eight embroidered textile electrodes attached laterally to a bed sheet for measuring bipolar contact electrocardiography from multiple channels. The textile-based sensing techniques should have greater potential to facilitate long-term monitoring with lower disturbance or discomfort.

Many of these motion-sensing techniques can extract signals of body motion in bed in an unobtrusive way. However, how to adapt these techniques to be viable for the home or nursing home in terms of complexity, cost, and comfort, remains a major challenge.

Some commercialized bed sensors for detecting movements in bed, bed occupancy, and fall events can be found in the current market [e.g. Telehealth Sensors LLC (Batavia, Illinois), Tunstall Healthcare Ltd (Yorkshire, UK)]. The functions of these products mainly focus on emergency event alert. When an abnormal event is detected, the product will alarm local caregivers for specific care services.

This paper presents the Bed-Centered Telehealth System (BCTS), which is designed to be used in homes or nursing homes. The core sensor of the BCTS is a commercialized soft motion-sensing mattress, WhizPAD, developed for unobtrusive sensing of body motion in the bed.17 Instead of adding sensing components into the bed, in WhizPAD the mattress itself is designed into a sensor using textile-based sensing techniques. WhizPAD collects signals of body motion in bed, which can be classified into events such as on/off bed, sleep posture (lying flat or lying side), movement counts, and respiration rate. Integrated with information and communication systems, caregivers can maintain awareness of older adults' daily activities and needs by using their mobile devices to access the BCTS for real-time monitoring and historical data record of bed-related activities, as well as receiving service reminders and alerts for abnormal events. Design, testing, and implementation of the BCTS in homes and nursing homes are described in this paper.

The BCTS has the potential to be extended for broader applications, including sleep quality monitoring and sleep environment control. Sensors for activity of daily living (ADL) monitoring can also be added. Instead of creating a brand new telehealth system for home users, the design concept of BCTS is to integrate telehealth functions into something that already exists in the home, namely the bed.

2. Methods

2.1. Design of the motion-sensing mattress WhizPAD

WhizPAD is a thin mattress pad made of memory foam and conductive textile materials. WhizPAD is designed into a mattress with motion sensing capability using the same material and fabrication process of the bedding manufacturer, so that the mattress is comfortable, flexible in use, easy to install, and low cost.

WhizPAD is in a sandwich structure of two pieces of foam, each 6—10 mm in thickness, on which conductive fiber is knitted in a

special pattern in the sensing area, with pieces of conductive foam in between. The working principle of WhizPAD is similar to that of a membrane switch for turning a circuit on and off. If there is no pressure applying on the WhizPAD, the top layer and bottom layer of WhizPAD do not contact with each other and results in an open circuit. Once the WhizPAD is under pressure, the top layer and bottom layer make contact with each other and create a close circuit. In addition to on/off detection, different pressure will create different contact quality between layers of conductive fiber and conductive foam, and therefore generates different resistance.

As shown in Fig. 1, the average resistance of 10 tests of a sensing unit decreases monotonically with applied pressure in the range of 1800—4300 Pa (the range of pressure caused by the presence of an adult) when measured on surfaces of different hardness. The standard deviation of the 10 tests of each data point is also specified in the figure. The special elastic foam provided by the bedding manufacture has passed the fatigue test of 30,000 pressure cycles.

With the advantage of textile-based sensor design, a sensing unit on the WhizPAD is flexible in sensing sensitivity, shape, size, and location. Several sensing units can be integrated into a Whiz-PAD for different applications. Fig. 2 shows a possible layout of the sensing units on the mattress, with three 80 cm x 40 cm horizontal sensing units for detecting movements of the upper limbs (sensing unit 1), hip (sensing unit 2), and lower limbs (sensing unit 3). The layout of the sensing units can be easily adjusted depending on the application.

Table 1 shows the specifications of the WhizPAD. The most important value of a mattress is its comfort. WhizPAD integrates with the body-shaped memory foam atop the sensing layer. The hardness and elasticity of the memory form changes with body temperature, which helps to decrease the stress applied on the skin. An experiment was conducted to evaluate body pressure distribution measured on WhizPAD. Ten testers, seven males and three females, weighing 58—87 kg were recruited for the experiment. In the first test, testers lay on the standard mattress of a nursing bed for 20 minutes. In the second test, the WhizPAD is put on top of the nursing bed and the testers lay on the WhizPAD for 20 minutes. As shown in Fig. 3, three Big-Mat sensor sheets (Nitta Corporation) were used to measure the body pressure distribution in the upper limb, hip, and lower limb areas. The average body pressure is 17.2% lower when the WhizPAD is put on top of a standard mattress of a nursing bed.

2.2. Bed-related event recognition

WhizPAD is connected to a bedside data processor for signal processing and data transmission. The bedside data processor

(Ohm, Q)

2000 2500 3000 3500 4000 4500

Pressure (N/m2, Pa)

Fig. 1. Relationship between the applied pressure and resistance of a sensing unit on surfaces of different hardness (solid line: floor; dotted line: elastic foam; cross line: memory foam).

Fig. 2. A possible layout of the WhizPAD.

Table 1

Specifications of the WhizPAD.

Characteristic

Specification

Length x weight x height Weight Major materials Operational voltage/current Environment temperature/humidity Sensing layer Sensor type

Response time Pressure sensing range Resistance range

188 cm x 90 cm x 6 cm 5.94 kg

Foam and conductive material DC 5 V/1 mA 0—50°C/30—80%, No condensation Piezoresistance 50/100/500/1000 ms 1800—4300 N/m2 3—1600 U

integrates the microchip Atmega644p, a 6-channel A/D converter, real-time clocks, micro SD storage, ZigBee transmission module, and Internet network module. The sensing data and events can be transmitted through the ZigBee transmission module, or stored in the SD card, which can be accessed via the Internet upon request by the remote caregivers. The sampling rate of the signals from WhizPAD is set at 40 Hz. The resistance of a sensing unit caused by the applied pressure is converted into a voltage signal using a corresponding divided circuit. Through a 10-bit output analog to digital converter, the resolution of pressure signal from a sensing unit is in the range of 0—1023.

Given the algorithms implemented in Atmega644p, the pressure signals collected by WhizPAD can be used to detect the

following four events: on/off bed, sleep posture, movement counts, and respiration rate. The experiments for evaluating the performance of WhizPAD were approved by the Institutional Review Board of Mackay Memorial Hospital of Taiwan (Taipei, 12CT042b). The detection of on/off bed on the WhizPAD can be easily recognized by using a simple pressure threshold. In the data acquisition process described above, the average noise of the pressure signals obtained from the bedside data processor is 10, and the pressure signal of a 30 kg object is about 500. Therefore "on bed" status is recognized when the sum of pressures signal from all three sensing units is >100.

When lying on the side, the pressure applied by the shoulder is higher than when lying flat. Therefore, the ratio of pressure signals obtained from sensing unit 1 (upper limbs) and sensing unit 2 (hip) defined as R12, is used to determine sleep posture of lying flat and lying on the side. In a calibration process with 20 individuals, 10 males and 10 females, weighing 40—90 kg, the average R12 of lying flat was 0.32 (s = 0.15), and the average R12 of lying side was 0.65 (s = 0.15). Finally a threshold of R12 = 0.6 is used to detect sleep postures of lying normal and lying on the side on the WhizPAD.

When the status is "on bed", change in pressure signal in each sensing unit is checked every 1 second to detect whether there is movements on the WhizPAD. As described earlier, the noise of the pressure signals obtained from the bedside data processor is about 10, and the amplitude of breathing signals is about 40. Therefore a "movement" is identified if the difference in pressure signals in 1 second from any of the three sensing units is larger than 80.

An experiment was designed to evaluate the performance of the event algorithms. In this experiment, 15 healthy testers, 7 males and 8 females, aged 20—30 years old, weighing 45—98 kg were recruited. Each tester followed a specific procedure: lying flat on the bed, turning to the left side, turning to the right side, then getting off the bed. Each position was maintained for 30 seconds, and the whole procedure was repeated 3 times for each tester (total case number is 45). The sensitivity of on-bed detection and off-bed detection are both 1.00; the lying flat and lying side detection in sleep posture detection are 0.79 and 0.92; the movement count detection is 1.00. Positive predictive value (PPV) of on-bed detection and off-bed detection are also both 1.00; the lying flat and lying side detection in sleep posture detection are 0.86 and 0.84; the movement count detection is 0.94. The sensitivity and PPV of recognizing these three events range from 0.79 to 1.00 in this experiment. Sleep posture detection has lower sensitivity and PPV.

Fig. 3. The body pressure distribution of a 76 kg tester measured on (A) a standard mattress of the nursing home, and (B) the WhizPAD using three Big-Mat sensor sheets (Nitta Corporation).

Table 2

The comparison of respiration rate (/minute) output by WhizPAD and PSG.

Fig. 4. Pressure signals from a dead weight and a living person collected from WhizPAD.

Fig. 4 shows the signals of physical activities in bed collected by the sensing unit of upper limbs of WhizPAD from a 60 kg silica gel model and an 80 kg male tester. In Fig. 4B, the respiration pattern can be seen clearly from signals collected by WhizPAD, whereas in Fig. 4A, the signals obtained from a dead weight put on the bed appear to be background noise. An algorithm is developed to determine the respiration rate from the pressure signals collected from WhizPAD. The main purpose is not to replace existing standard, accurate medical equipment for determining respiration rate, but to be able to distinguish from a deadweight and a living person lying on the WhizPAD.

A procedure of signal processing is performed to determine the respiration rate from the pressure signals collected by WhizPAD. First the pressure signals are filtered by a 10-point averaging filter. According to the slope, the filtered signals are then transformed into a series of 1 (positive slope) and 0 (negative slope). In clinical practice, polysomnography (PSG) recording is used as the standard equipment for sleep quality evaluation. The BWII PSG from Sleep Virtual is used in this study. It is Type IAASM compliant, composed of 29 channels of parameters, and its maximum sampling rate is 1000 Hz and signal resolution is 12 bit. Fig. 5 shows the comparison of respiration signal collected by WhizPAD and by a thorax sensing belt of PSG. A complete respiration cycle can be extracted from the series of 0/1 data, and the period of the respiration cycle can be calculated. The WhizPAD then outputs the average respiration rate every 20 seconds.

Ten testers, 8 males, 2 females, aged 20—30 years, weighing 45—90 kg were recruited in an experiment for evaluating the accuracy of respiration rate determined by the WhizPAD. Each tester wore a thorax sensing belt of PSG to measure changes in thorax during respiration. When the experiment started, each tester lay on

Tester Test PSG WhizPAD Difference

A 1 11.89 11 +0.89

2 10.83 10 +0.83

3 8.79 8 +0.79

4 8.63 8 +0.63

5 10.46 9 + 1.46

B 1 6.97 7 -0.03

2 9.03 8 +1.03

3 10.16 9 +1.16

4 11.83 12 -0.17

5 10.71 10 +0.71

C 1 11.52 10 +1.52

2 11.93 10 +1.93

3 9.95 8 +1.95

4 8.40 8 +0.83

5 7.23 7 +0.23

D 1 6.95 7 -0.05

2 8.02 7 +1.02

3 7.75 7 +0.75

4 7.17 8 -0.83

5 8.93 8 +0.93

E 1 10.57 10 +0.57

2 10.56 10 +0.56

3 11.72 11e +0.72

4 10.78 9 +1.78

5 11.01 11 +0.01

F 1 6.40 6 +0.40

2 8.58 8 +0.58

3 7.94 8 -0.06

4 6.79 6 +0.79

5 7.86 7 +0.86

G 1 11.71 12 -0.29

2 10.12 10 +0.12

3 11.01 10 +1.01

4 10.49 9 + 1.49

5 10.29 10 +0.29

H 1 8.49 8 +0.49

2 9.35 10 -0.65

3 8.89 8 +0.89

4 8.80 9 -0.20

5 9.45 9 +0.45

I 1 11.00 11 +0

2 6.49 7 -0.51

3 10.19 9 +1.19

4 10.63 10 +0.63

5 7.68 7 +0.68

J 1 13.49 13 +0.49

2 12.31 12 +0.31

3 13.01 12 +1.01

4 10.20 10 +0.20

5 10.09 10 +0.09 +0.63

the WhizPAD and breathed normally for 1 minute. Respiration signal measured can be displayed and stored in the computer for further processing. The whole procedure was repeated five times for each tester (total case number is 50).

(A) The measured signal of respiration from PSG

(B) The analyzed signal of respiration from WhizPAD

0 - mjMiiinujTiu^ ii !:::::::: ii ::::::::: ii ::::::::: ¡1 ::::::::: i ::::::: :

0 :::::::: 200: :::::::: 400: ::::::: : sp»!! ::::::: soo: ::::::: :iqoo: ::::::: iSoo

Fig. 5. The comparison of respiration signals from WhizPAD and polysomnography.

Table 3

The confidence interval for evaluating the mean of difference between WhizPAD and polysomnography.

Confidence level 100 x (1 — a/2)% Lower bound Upper bound

90 0 1.17

95 -0.12 1.28

97.5 -0.22 1.38

99 -0.33 1.50

The respiration rate output from the WhizPAD was then compared with the integer number of complete respiration cycle detected by PSG. As shown in Table 2, the average difference of respiration rate determined by the WhizPAD is 0.63/minute higher than the integer number of complete respiration cycles detected by

PSG. Inferential statistical analysis was also used to estimate the difference between WhizPAD and PSG under different confidence levels. Table 3 shows the confidence intervals of mean of difference between WhizPAD and PSG, under 90%, 95%, 97.5%, and 99% confidence levels. (See Appendix 1 for details of calculating the confidence interval.) From the test, we can see that the respiration rate detected by WhizPAD is higher than the integer number of respiration detected by PSG, but the difference is not more than 2.

3. Results

The application scenarios of the BCTS based on the WhizPAD used at home or in a nursing home are described in the following sections.

Fig. 6. Communication structure of the Bed-Centered Telehealth System in a home application.

Fig. 7. Communication structure of the Bed-Centered Telehealth System in a nursing home scenario.

3.1. Home application of the BCTS

The home version of the BCTS has been commercialized and sold in department stores in Taiwan. Fig. 6 shows the communication structure of the BCTS home application scenario. WhizPAD is put on the bed of the older adult in the home environment. The bedside processor is plugged directly into a home router for Internet connection. No special setup of the bedside processor is required. Remote caregivers can access the bedside processor via the Internet to browse real-time and historical data record from the WhizPAD app on their mobile devices.

A WhizPAD app is developed for the remote caregivers on their mobile devices. For real-time sleep monitoring, the WhizPAD app displays on/off bed status, sleep posture, number of movements in bed in the past 1 minute, and the time of the last movement. The remote caregivers can also browse historical data records from the WhizPAD app in either graphical or text format. In addition to browsing data, the WhizPAD app provides an alert function to the remote caregivers if abnormal events are detected. According to the parameters set by the remote caregiver (events, monitoring period, and frequency), the WhizPAD app connects to the bedside processor to request data automatically. If a preset abnormal event such as "leave bed during the night" or "low activity in bed" is

detected, the WhizPAD app will pop a reminder message to alert the remote caregiver.

3.2. Nursing home application of the BCTS

The BCTS has been implemented in a nursing home in Taiwan. A total of 30 beds are equipped with WhizPADs. Fig. 7 shows the communication structure of the BCTS in the nursing home setting. The bedside processor that accompanies the WhizPAD serves as an end device of a ZigBee wireless sensor network established in the nursing home. The monitoring data for each resident is transmitted directly to the remote server by a coordinator of the ZigBee wireless sensor network for the data management and service administration. Intermediate ZigBee routers can be deployed if the distance between end devices and the coordinator is too great.

Integrated with a care management system, the messages received from the bedside processor can be displayed on the information board at the local nursing station to facilitate real-time monitoring and alerts, service reminders, and browsing the historical data record. The nursing staff can keep aware of whether a resident is on the bed, as well as when to turn the resident's body over or pat his/her back for disabled residents who cannot leave beds. The nursing staff can also query the data for a particular

Fig. 8. Data collected by the Bed-Centered Telehealth System: (A) a healthy resident, and (B) a resident with frequent body shaking and twitching.

resident from their mobile devices. Physical activities in bed and classified events are stored in the historical database and could be used not only in the management of the particular resident but for administrative purposes such as ensuring that adequate staff is on duty.

Figs. 8 and 9 are the sample data of residents collected by the BCTS in the nursing home. These figures show bed-related activities of four residents with different conditions in a typical day, including the on/off bed status and the number of movements in bed/minute. Two bed-related indices, the average number of body movements in bed/minute and the percentage of in-bed time in a day are also displayed. Fig. 8A shows the data of a healthy resident in a typical day, and Fig. 8B shows the data of a resident with frequent body shaking and twitching. Both residents have a very similar on/off bed pattern but the average number of body movements in bed/minute for the resident in Fig. 8B is much higher. Fig. 9A shows the data of a disabled resident who cannot leave the bed. There are intense physical activities in bed in regular periods (around 2 hours), which are actually the care services of body turning over, to relieve the pressure and prevent complications such as bedsores. Fig. 9A shows a completely different data pattern obtained from a dementia resident.

4. Discussion

This paper describes the BCTS, which uses the bed as the center of health data collection of telehealth systems implemented in homes and nursing homes. The core sensor of the BCTS is a soft motion sensing mattress, WhizPAD. The BCTS facilitates bed-related realtime monitoring (on/off bed status, sleep posture, body movements), service reminder, and historical data record. Caregivers can also use mobile devices to access the data collected by WhizPAD.

Compared with products existing in the current market, in WhizPAD the whole mattress itself is designed into a sensor using textile-based sensing techniques, instead of adding sensing components into the bed. In addition to an emergency alert to local care givers, WhizPAD also emphasizes long-term telemonitoring of sleep pattern, respiration, and sleep quality.

Centered around the bed, the BCTS has the potential to be extended for broader applications. The following functions are being developed. (1) Sleep quality monitoring in the home environment. PSG is considered to be the gold standard method for assessing sleep. Different sleep stages are evaluated using PSG data such as electroencephalogram, electro-oculogram, and electromyogram. However, it carries high equipment cost and can be operated only by a

lpm 3pm 5pm 7pm 9pm 11pm lam 3am 5am 7am 9am 11 am

Fig. 9. Data collected by the Bed-Centered Telehealth System: (A) a disabled resident, and (B) a dementia resident.

professional. Benchmarking with the sleep status identified by the PSG, we are developing an algorithm to classify the sleep/awake status of the user from the data collected by WhizPAD, so that some important sleep quality indicators such as sleep latency, sleep duration, sleep efficiency and sleep disturbance can also be reported by the BCTS. (2) Shaping a perfect sleep environment. Sleep can be affected by the immediate environment, including lighting, noise, and temperature. KNX is the worldwide standard communication protocol for all applications in home and building control (ISO/IEC 14543-3).18,19 A centralized BCTS could be integrated with KNX to form a building automation application that could control appliances according to sleep status detected by the BCTS. For example, dim the light and raise the temperature of the air conditioner if the BCTS detects that the user has fallen asleep. (3) More sensors can be added into the BCTS for activity of daily living monitoring. ADLs refers to tasks that are required for personal self-care and independent living, such as eating, dressing, cooking, drinking, and taking med-icine.20 The performance of daily activities has been widely used in clinical and research fields as a measure of disability, or functional status of elderly people. Additional sensors for ADL monitoring, such as infrared sensor for human movements and electric current sensor for electrical appliance usage, are being integrated with the bedside device of the WhizPAD, so that the BCTS can extend telehealth care from sleep monitoring to activity of daily living monitoring.

Conflicts of interest

No competing financial interests exist. The funding source did not influence study design, data collection, data analysis, interpretation, or presentation.

Acknowledgments

Support for this work was provided by the National Science Council, Taiwan, under contract NSC 100-2622-E-155-004-CC3. Support from SEDA Chemical Products Co., Ltd. is gratefully acknowledged.

Appendix 1. Confidence interval

A "confidence interval" gives an estimated range of values that is likely to include the underlying population parameter if independent samples are taken repeatedly from the same population, with the specified level of probability. Under the assumption that the two observations are normally distributed, if both multiple samples of m groups each of size n are given as xij and yij, where i = 1, 2,..., m and j = 1, 2, ..., n. and the sample sizes are large enough, the 100 x (1 — a/2)% confidence interval for mean difference of two observations is:

x - y - Z1_f

1_. mn

x - y + z1_

2 y mn

Em _ m=1X m

Xi = 1

S=1 (xv-

j=1yj yi =—-=—

S2 ^ Z^i=1S2i

J2U(y-- yi)2

and Z1-a is the lower 1 -1 quantile of a Z distribution satisfying

pr{ z < zm} =1 - 2.

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