Authors: Yuchen-Huang (2014-07-30); recommended: Yeh-Liang Hsu(2014-08-01). Chapter 4. The sleep and activities of daily living (ADL) monitoring application Long-term ADL profiles of the older adults acquired at home environment can provide additional comprehensive information related to the living behaviors, and thus their functional abilities can be better understood. Advances in sensors and ICT tools have the potentials to assist ADL measurements in an unobtrusive way without disturbing the daily life of the older adults. This chapter describes the application of the home teleheleath system based on social networking for sleep and ADL monitoring in the home environment. The emphasis is on how to convert the monitoring data into meaningful messages to be conveyed on the social networking sites such as Facebook. Section 4.1 first briefly introduces the monitoring devices used in this research, including the human movement detectors, the temperature / humidity detectors, the appliance usage detectors and WhizPAD for sleep monitoring. In section 4.2 and 4.3, a selection of indices is defined for further assessing sleep and ADL performances, including indices for daily report and long-term life pattern. The indices are implemented in an App which can be installed on the users mobile devices such as tablets and smartphones. The alert messages and monitoring reports can then be sent to Facebook. Finally, in section 4.4 the way to sharing sleep and ADL monitoring report from mobile device to Facebook were described. 4.1 Introduction of the monitoring devices (1) WhizPAD An integrated motion sensing mattress for sleep monitoring For older adults living at home, the bed is an indispensable part of their daily lives. Bed activity monitoring provides valuable information of the status for the older adults. In this home application, WhizPAD (Figure 4-1), the mattress itself is designed into a sensor using textile-based sensing techniques and collects signals of physical activities in bed. 1
The signals from WhizPAD can be classified into events such as on/off bed, sleep posture, movement counts, and respiration rate. By integrating with some ICT tools, the system can provides some sleep monitoring and assessing functions, such as real time sleep monitoring, sleep reports etc. Figure 4-1. WhizPAD (2) The human movement / temperature / humidity detector In the home environment, ADL monitoring systems usually consist of a range of simple and low-cost sensors. The passive infrared (PIR) sensor is the most common sensor for detecting human occupancy or for determining active movements within a sensitive range of specific space. Mobility changes can be observed and identified from a multi-sensor monitoring system utilizing PIRs at home [43]. In this application, the human movement detectors sense the changes of human infrared intensity which represent occurrences of active movements within a sensible range. Once activities are detected by the human movement detector, it transmits the signals (activity count) to the Distributed Data Server (DDS) via a ZigBee 2.4GHz wireless protocol. Besides, the human movement detector also measures room temperature and humidity. If an activity is detected, the detector reports the activity count, temperature and humidity. However, if there is no activity, the detector will report the temperature and humidity every one minute. Figure 4-2 is the human movement / temperature / humidity detector. 2
Figure 4-2. The human movement / temperature / humidity detector (3) The appliance usage detector The appliance usage detector is used to detect the on/off state of an appliance, such as a TV set. A specific threshold of AC current is given to determine the on/off status of the appliances, indicating whether the appliances are in use or not. The count signals will be sent to the DDS periodically. Figure 4-3 is the appliance usage detector. Figure 4-3. The appliance usage detector 4.2 Information structure of the sleep and ADL monitoring application Figure 4-4 illustrates the home telehealth system structure based on social networking for sleep and ADL monitoring application. All sensors such as WhizPAD, the human movement / temperature / humidity detector and the appliance usage detector are embedded in the home environment, so that the older adults may not be aware of the sensing actions taking place. Distributed data servers (DDS) is the core for sensing data collection. Sensing data from detectors are transmitted via Zigbee to the DDS. The DDS is 3
scheduled to report its present IP address to a pairing system in the cloud periodically because its IP may change in the dynamic IP setup. When user logs into the WhizPAD App, the App will generate a pairing system URL connection and send the WhizPAD device ID and password, which are typed by the user via Wifi or 3G. After confirming the information, pairing system will respond the current DDS IP and port to App by URL connection. During this data transmitting process, all of the data are encrypted and transmitted in the form of JavaScript Object Notation (JSON) which is a common and lightweight data interchange format. Figure 4-5 is the simple display interface of the pairing system. Figure 4-6 shows the sleep data in JSON format. For children, family members and caregivers, the monitoring Apps such as WhizPAD App and ADL App can be downloaded from the application platform Google Play and installed on their mobile devices. Mobility changes and monitoring data can be displayed by graphical and tabular way in the App. Figure 4-4. The structure of sleep and ADL monitoring application 4
Figure 4-5. The simple display interface of the pairing system Figure 4-6. The real-time monitoring data of WhizPAD in JSON format The Facebook Graph API and other official software development kits (SDK) allow developers to integrate their products or services with Facebook. Each service needs to apply a Facebook application ID, and each Facebook application ID represents one service or product. Facebook uses a variant Open Authentication (OAuth) protocol that allows users to approve application to act on their behalf without sharing their passwords. When user connect with a service using Facebook Login, the service will be able to obtain an access token which provides temporary, secure to access user s Facebook public profile. In 2011, Facebook implemented the Secure Socket Layer (SSL) encrypt protocol to provide communication security over the Internet which ensures that the information passing through the other services is not open for access for everyone. In the CDF application structure of Chapter 3, the tablet plays the role of DDS for receiving sensing data. It is also connected to Facebook for data storage and display. However, the DDS in Figure 4-4 is a micro-processor based data server (Raspberry pi in the case of WhizPAD) that cannot be connected with Facebook because it can not offer SSL encrypt protocol. The other possibility is to use pairing system in Figure 4-4 to 5
connect to Facebook. However, when the messages are posted to Facebook by the same IP or same application ID at the same time or in short time duration, this service will be blocked. Under these limitations, the next section describes how in this application, children, family members and caregivers set up the rule of alert messages and send to social network automatically via the mobile devices. 4.3 Converting sleep monitoring data into meaningful messages to be conveyed by Facebook The section first describes how the monitoring data is converted into meaningful messages to be conveyed by Facebook. ADL monitoring data has been well studied by Yang et al [44]. Therefore this section focuses on sleep monitoring data. For sleep monitoring, indices for daily report and long-term life pattern are developed. For daily sleep report, three components of Pittsburgh Sleep Quality Indices (PSQI) including sleep latency, sleep duration, habitual sleep efficiency are used. For long-term life pattern, seven-day average of on-off bed status is accumulated. For a given day, the correlation coefficient is presented to describe the strength of correlation of the day with the average on-off bed profile for the seven-day duration. 4.3.1 Indices for daily report and long-term life pattern for sleep monitoring In this research, five participants (55-90yrs) were recruited for sleep monitoring data collection. Before the experiment, WhizPAD were installed in their rooms to detect and transmit the sensing data to their own DDS. (1) Three components of PSQI The most commonly used measure of sleep quality is the PSQI, which evaluates sleep quality with seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Three PSQI components, namely sleep latency, sleep duration, and habitual sleep efficiency, can be estimated through the asleep/awake detection of WhizPAD. Figure 4-7 is an on-off and awake bed status example. The graph was calculated and implemented according to the algorithm, which was first presented by Cheng et al [46]. In this example, the sleep latency is 6 minutes that represents participant spent 6 minutes to lie on the bed 6
and fall asleep; the sleep duration is 67 minutes that represents the length of participant when he/she falls asleep. Finally, the habitual sleep efficiency is 67% (sleep duration divided by the length of participant lies on the bed). Figure 4-7. An example of on-off / awake bed status monitoring data (2) On-off bed profile for a single day Figure 4-8 shows sleep monitoring data of participant A (56yrs, employed female) for two different dates (on May 05 and May 07 in 2014). The data logging interval (epoch) is 5 minutes. Therefore there are 288 epochs from 0:00 to 23:59 per day. The value of y axis represents the on-off bed status (1: on-bed / 0: off-bed); x axis represents the time. 7
Figure 4-8. The on/off bed status of participant A at two different dates (3) Average on-off bed profile for seven days On-off bed profile can also be observed on a long-term basis. Figure 4-9 shows the average on-off bed profile of participant A for seven days (from May 04 to May 10, in 2014). Standard deviation is 0.02. Note that the range standard deviation is between 0 to 0.5, and is an indication of whether the in-bed pattern has high regularity (low standard deviation). During the seven days, the average in-bed percentage is 41%, and the average number of movements when in bed is 3.7 times / min, which is a general indication of whether the participant sleeps well. Figure 4-9. The average on-off bed status data of participant A for seven days (May 04 to May 10, 2014) Figure 4-10 shows the average on-off bed profile of participant B (60yrs, employed male, two graveyard shifts in a week) for seven days (from April 1 to April 7, 2014). Standard deviation is 0.08, which is higher than that of participant A, indicating that the in-bed pattern of participant A is more regular than participant B. 8
Figure 4-10. The average on-off bed status data of participant B for seven days (April 01 to April 07, 2014) These observable trends of on-off bed status monitoring profile obtained by the WhizPAD are consistent with the rhythm of daily living of participant A and B. (4) Correlation coefficient of a given day The strength of correlation in in-bed pattern between a single day and a week can also be observed through the correlation coefficient value from the on-off bed status data. Figure 4-11 shows the average on-off bed status data of participant A for seven days (May 04 to May 10, 2014) and a single day (May 11, 2014). The correlation coefficient value of two curves is 0.96, which indicates the in-bed pattern of participant A on this single day is similar to the in-bed pattern of the previous week. Figure 4-11. The average sleep monitoring data for a week (May 04 to May 10, 2014) and a single day (May 11, 2014) of participant A 4.3.2 Sharing sleep and ADL monitoring report to Facebook 9
After presenting the detectors, information structure and the selection of indices in this home environment applications, the way to sharing sleep and ADL monitoring report from mobile device to Facebook were described in this section. Figure 4-12 is the simulating user interface of sleep quality function in WhizPAD App. The top graph in this simulation interface displays the average on-off bed status profile of seven days, which is calculated and transmitted from the DDS (server) in order to reduce the calculation loading of mobile devices. In this example, the duration of average on-off bed status data of seven days will start on May 10 (the day before May 11 which is selected by the user) and end on May 04. The second graph displays the on-off bed data of the give-day. Other value of indices which mention in Section 4.3 are listed and displayed below these two graphs. Figure 4-12. The simulating interface of sleep quality in WhizPAD App When user press the Share information button on the top menu, the information of indices will be sent to the older adult s Facebook timeline automatically. Other family members, caregivers can read this message by using Facebook App on their mobile devices 10
or browsing Facebook website if the older adult is one of their Facebook friends. Figure 4-13 shows the message on older adult s Facebook timeline. Figure 4-13. The message is posted from WhizPAD App to older adult s Facebook timeline Reference [43] Chan, M., Campo, E., & Estève, D. (2005). Assessment of activity of elderly people using a home monitoring system. International Journal of Rehabilitation Research, 28(1), 69-76. [44] Yang, C. C., & Hsu, Y. L. (2012). Remote monitoring and assessment of daily activities in the home environment. Journal of Clinical Gerontology and Geriatrics, 3(3), 97-104. [45] Cheng, C. M., Hsu, Y. L., & Young, C. M. (2008). Development of a portable device for telemonitoring of physical activities during sleep. Telemedicine and e-health, 14(10), 1044-1056. 11