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Authors: Yu-Wei Liu, Yeh-Liang Hsu (2014-01-15); recommended: Yeh-Liang Hsu (2014-09-09).
Note: This paper was presented in 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1466-1470, Manchester. DOI:10.1109/SMC.2013.253

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

Abstract—Given the rapid increase in the aging population and the decline in birth rate, there is a growing demand for healthcare services. For the elderly who are living at home or in nursing homes, the bed is an integral part of their daily lives. The monitoring of physical activities in bed can provide valuable information of the status of an elderly person. This paper presents a Bed-Centered Telehealth System (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. WhizPAD collects signals of physical activities in bed, which can be classified into events such as on/off bed, sleep posture, pressure distribution, movement counts, and respiration rate. The BCTS facilitates bed-related real-time monitoring, service reminders for caregivers, and a history of the user’s data. Integrated with information and communication systems, caregivers can maintain awareness of elderly persons’ daily activities and needs by using their mobile devices to access the WhizPAD and further provide the necessary care for them.

Keywords: motion-sensing; telehealth system.


Given the rapid increase in the aging population and the decline in birth rate, there is a growing demand for healthcare services, including medical support, behavior assistance, daily care, etc. For the elderly 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, the monitoring of physical activities in bed provides valuable information of the status of an elderly person.

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Many care systems have been developed based on activities detected in bed, for example, detection of bed-exit and fall events [Yonezawa et al. 2005, Ogawa et al. 2008, Bruyneel et al. 2011], recognition of sleep pattern and quality [Watanabe et al. 2005, Choi et al. 2007, Cheng et al. 2008, Migliorini et al. 2010], and the monitoring of obstructive sleep apnea syndrome (OSAS) [Watanabe et al. 2010, Bruyneel et al.2013]. 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 al. [1997] presented the idea of a robotic bed, which is equipped with 221 pressure sensors for monitoring respiration and body position. Van Der Loos et al. [2003] 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 al. [1984] presented a design of the static charge-sensitive bed (SCSB) for long-term monitoring of respiration, heart rate, and body movements. Kaartinen et al. [2003] used the SCSB method to determine the relation between movements in bed and sleep quality. Watanabe et al. [2005] designed a pneumatics-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 are measured as changes in pressure using a pressure sensor. 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 physical activities. Cheng et al. [2008] proposed a portable device for telemonitoring physical activities to evaluate body movements with quantitative measurement and to recognize sleep pattern and quality. Carvalho et al. [2008] developed textile and polymers 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 al. [2012] proposed an integrated system that uses eight embroidered textile electrodes attached laterally to a bed sheet for measuring bipolar contact electrocardiography (ECG) 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 physical activities in bed in an unobtrusive way. However, how to adapt these techniques to be viable for the home or nursing home remains a major challenge.

This paper presents the Bed-Centered Telehealth System (BCTS), which is based on a commercialized motion-sensing mattress WhizPAD and is designed to be used at home or in a nursing home. 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.

The core sensor of the BCTS is a soft motion-sensing mattress, WhizPAD, developed for unobtrusive sensing of physical activities in the bed [Liu et al. 2012]. Instead of adding sensing components into the bed, in WhizPAD the mattress itself becomes a sensor using textile-based sensing techniques. WhizPAD collects signals of physical activities in bed, which can be classified into events such as on/off bed, sleep posture, pressure distribution, movement counts, and respiration rate. By being integrated with information and communication systems, the BCTS can provide telehealth functions including real-time sleep monitoring, care service reminder, and historical data record.

Section 2 describes the design of the motion-sensing mattress, WhizPAD. Section 3 explores the application of the Bed-Centered Telehealth System in the home, and Section 4 considers the use of the BCTS in a nursing home. Finally, Section 5 discusses possible future extensions of the BCTS and concludes the paper.

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. As shown in Figure 1, the average resistance of 10 tests of a 20 cm × 20 cm sensing area decreases monotonically with applied pressure in the range of 500-3,500 Pa (the pressure caused by the presence of an adult). The special elastic foam provided by the bedding manufacture has passed the fatigue test of 30,000 pressure cycles. Figure 2 shows a possible layout of the mattress, with three horizontal sensing areas for detecting movements of the upper limbs, hip, and lower limbs, and three vertical areas for detecting movements of the trunk.

In order to enhance the comfort and decrease the occurrence of bedsores, WhizPAD integrates with the body-shaped memory foam that is atop the sensing layer. The hardness and elasticity of the memory form changes with body temperature, which helps to decrease the stress (<32 mm Hg) applied on the skin. Highly responsive foam is used in the bottom layer of WhizPAD, so that the sensing performance of WhizPAD is not affected by the type of material of the base mattress on which it is placed.


Figure 1. Relationship between the applied pressure and resistance of sensing units


Figure 2. A possible layout of WhizPAD

WhizPAD is connected to a bedside data processor for signal processing and data transmission. The bedside data processor integrates the microchip Atmega644p, a 6-channel A/D converter, real-time clocks, micro SD storage, ZigBee transmission module, and Internet network module. The sampling rate of the signals from WhizPAD is set at 10 Hz. Given the algorithms implemented in Atmega644p, the signals collected by WhizPAD from physical activities in bed can be used to detect the following five events: on/off bed, sleep posture, pressure distribution, movement counts, and respiration rate. The sensing data and events can be transmitted through ZigBee transmission module, or stored in the SD card, which can be accessed via the Internet upon request.

Figure 3 shows the signals of physical activities in bed collected by WhizPAD from a 60 kg silica gel model and a 80 kg male participant. In Figure 3(b), the respiration pattern can be seen clearly from signals collected by WhizPAD, while in Figure 3(a), the signals obtained from a dead weight put on the bed appear to be background noise.


Figure 3. Signals of physical activities in bed collected by WhizPAD

Table 1 shows the specifications of the WhizPAD. The BCTS can be used at home or at a nursing home. The application scenarios are described in the following sections.

Table 1. Specifications of the WhizPAD




188 cm × 90 cm × 3.5 cm


5.94 kg

Major materials

Foam and conductive material

Operational voltage / current

DC 5V / 1 mA

Environment temperature /


0 ~ 50 °C /

30 to 80%, No condensation


Sensing layer

Sensor type


Response time


Pressure sensing range

500 ~ 3500 (N/m2)

Resistance range

40 ~ 140 (Ohm)

Home application of the BCTS

Figure 4 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 from the WhizPAD App on their mobile devices.

Dynamic IPs are often used in the home environment. As shown in Figure 4, the bedside processor 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 bedside processor from the pairing system when requesting data. In the meantime, the pairing system also facilitates connection with social network platforms such as Facebook, for reporting historical readings and events to specific caregivers.


Figure 4. Communication structure of the Bed-Centered Telehealth System in home application

Figure 5 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 1 minute, and the time of the last movement. The user can also browse historical data from the WhizPAD App in either graphical or text format.

Data monitoring

Figure 5. The display interface of the WhizPAD App

Nursing home application of the BCTS

The BCTS has been implemented in a nursing home in Taiwan [Liu et al. 2011]. Figure 6 shows the communication structure of the BCTS in such a setting. The bedside processor that accompanies a 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 server (which also serves as the coordinator of the Zigbee wireless sensor network) at the nursing station. Intermediate Zigbee routers can be deployed if the distance between end devices and the coordinator exceeds the design limit.

The messages received from the bedside processor will be displayed on the information board at the nursing station to facilitate real-time monitoring and alerts, service reminders, and augmenting the historical data record. Figure 7 shows the main interface of the information board. The information helps the nursing staff to keep aware of whether a resident is lying 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 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.

Figure 6. The Bed-Centered Telehealth System in a nursing home scenario


Figure 7. The information board at the nursing station

Figure 8 shows sample historical monitoring data for two conditions of resident. Figure 8(a) shows historical monitoring data for a typical day of a healthy resident, including the on/off bed status (red line) and the number of movements in bed per minute (blue line). These records can indicate how well a resident sleeps. Figure 8(b) shows historical monitoring data for a typical day of a disabled resident who cannot leave the bed. There are intense physical activities in bed in regular periods. These activities are actually the care service of body turning over, to relieve the pressure and prevent complications such as bedsores. This monitoring data can be used as service record of the nursing staff for management purpose.


Figure 8. The historical monitoring data for (a) a healthy elderly person, and (b) a disabled elderly person

Discussion and conclusion

This paper described a Bed-Centered Telehealth System (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 real-time 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.

The BCTS has the potential to be extended for broader applications, including the following:

(1)  Sleep quality monitoring in the home environment

The polysomnographic (PSG) examination is the standard procedure for research into and clinical diagnosis of sleep disorders. However, it carries high equipment cost and can be operated only by a professional. By employing an algorithm designed for sleep monitoring, WhizPAD could detect the most important indicators of sleep quality, such as frequency of turning over and duration of on-bed status. It can help not only to evaluate sleep quality but to record a person’s sleeping history as a reference for further diagnosis.

(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. 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.

(3)  More sensors for activity of daily living (ADL) monitoring

Activities of daily living (ADLs) refers to tasks that are required for personal self-care and independent living, such as eating, dressing, cooking, drinking, and taking medicine [Katz et al. 1963]. 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. If additional sensors for ADL monitoring and event algorithms were integrated with the bedside device of the BCTS, the BCTS could extend telehealth care from a smart bed to a smart architecture.


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