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Authors: Che-Chang Yang, Yeh-Liang Hsu (2007-03-16)recommended: Yeh-Liang Hsu (2007-08-06).
This paper is presented at the 3rd IASTED International Conference on Telehealth: Telehealth 2007, May. 30-Jun. 1, 2007, Montréal, Canada

Developing a Wearable System for Real-time Physical Activity Monitoring in a Home Environment

Abstract

Quantitative assessment of daily physical activity in a home environment provides significant information in evaluation of health status and the quality of life of subjects with limited mobility and chronic diseases. This study developed a home telehealth-based application, a wearable system for real-time human physical activity ambulatory monitoring by using only one mobile sensing device which utilizes tri-axial accelerometry measurement and the distributed data processing modality. This system is able to identify several targeted human postures, postural transitions and walking with the embedded algorithm. In addition, this system also features fall detection capability which might be highly desired for elderly care. The results of the test for evaluating the performance of the system show high identification accuracy for both still postures and dynamic movements. A long-term ambulatory test at home was also demonstrated and the recorded data indicated sufficient information regarding the subject’s activities of daily living. Some inherent limitations concerning real-time identification were discussed. Despite the limitations, this system is technically viable for ambulatory application to provide sufficient information in evaluating a person’s activity of daily living (ADL) and his physical mobility level. Potential wok of this system in the future is also discussed.

Key words: Physical activity, fall detection, tri-axial accelerometer, home telehealth, ambulatory monitoring

1.     Introduction

Physical activity can be regarded as any bodily movement or posture that is produced by skeletal muscles and results in energy expenditure [Capspersen et al., 1985]. Various health conditions such as heart disease, senile dementia, degeneration in mobility will directly affect one’s physical activity level. However, a majority of assessment methods used in the past mostly relied on questionnaire registration and observation, which may possibly lead to insufficient information and inconsistent assessment results [Wada, et al., 2005]. Therefore, quantitative assessment of daily physical activity at home is a determinant in the evaluation of health and the quality of life of subjects with limited mobility and chronic diseases, such as elderly persons [Foster et al., 1999].

Physical activity assessment is difficult due to the subtle and complex nature of body movement which requires precise and reliable monitoring approaches. Current developed physical activity monitoring systems can be technically classified according to the adopted sensing techniques. Ambient sensor arrays, such as various kinds of switches, have been widely used for ADL (activity of daily living) acquisition [Noury et al., 2002, 2003]. Such system architecture can provide continuous monitoring in non-intrusive way. However, the inability in accurate dynamic motion analysis has long been the major drawback that can not be completely overcome. Although video cameras can be employed to enhance the accuracy, such method may lead to the demanding issue of personal privacy in free-living environment.

The other similar sensing technique is the use of human motion capturing systems which have been widely used in the applications of computer animation and virtual reality. These systems are based on optical, magnetic and ultrasonic operation principles and can allow a complete kinematic analysis but require a dedicated laboratory site [Aminian et al., 2004], [Bodenheimer et al., 1997], [Disckstein, et al., 1996], [Aminian et al., 2002], [Kemp et al., 1998]. However, complex system installation and higher cost of system facility are the major factors unacceptable for common use in home environment. Moreover, the subjects under monitoring must be restrained inside a laboratory-like space, which is entirely different from a free-living home environment.

The related research in recent years has been focusing on developing wearable systems, which has been regarded as appropriate alternative for physical activity monitoring [Bouten et al, 1997], [Meijier et al., 1991], [Najafi et al., 2002], [Veltink et al., 1996], [Karantonis et al., 2006]. In a wearable system, the sensor units (e.g., gyroscopes or accelerometers) and required components are integrated into portable devices, or are even incorporated into clothing. With advancing technologies in microcontrollers and wireless communication, real-time identification for physical activity was achieved onboard the wearable system, without the need of off-line data analysis in external computation phase [Karantonis et al., 2006].

The development of home telehealth system is an emerging trend in societies of rapid aging population [Scanaill et al., 2006]. Tele-monitoring basic health parameters provides safe, cost-effective, efficient and patient-centered healthcare to improve clinical outcomes at lower cost [Finkelstein et al., 2006]. The portable tele-homecare monitoring system (PTMS) was developed by the authors and was introduced for product-oriented design. What sets this innovation apart from most other systems is its highly decentralized monitoring model and the portable nature of the system. Such cost-effective and product-oriented approach makes the system economically viable and acceptable to the end-users [Hsu et al., 2007].

The purpose of this research is to demonstrate a PTMS-based system for real-time monitoring and preliminary assessment of physical activities at home. The ambulatory monitoring is achieved by capitalizing on real-time accelerometry measurement and processing of single wearable device. A hierarchical algorithm was developed and embedded in the wearable device to enable real-time identification of still postures (lying, sitting and standing), postural transitions (sit-stand, lie-sit) and walking. This system also features the function of fall detection and its corresponding emergency alarming reports (GSM phone message or e-mail), which may be highly desired for the elderly care. Based-on home telehealth application, this system is implemented to register one’s long-term physical activity data at home for preliminary assessment of mobility level.

2.     Methods and system design

2.1 Instrumentation

Figure 1 shows the structure of the system. A wearable motion detection unit (MDU) primarily consists of a miniature tri-axial accelerometer module (KXM52-1050, Kionix, Inc.), a PIC microcontroller (PIC18F6722, Microchip) and a RF wireless transmitter module (PT2262, Princeton Tech.; TWS-CS-2, Wenshing Electronics Co., LTD). It is designed for accelerometric measurement and real-time identification of human postures and movements. Figure 2 shows the package of MDU and its tri-axial orientation of acceleration measurement. The MDU uses a gravity-responsive tri-axial accelerometer to measure acceleration and inclination produced by human movement or posture. The sensor outputs are initially low-pass filtered at 50Hz for noise rejection and are then continuously sampled at 60Hz via 10-bit A/D conversion of the PIC microcontroller. Real-time identification is simultaneously processed in the PIC microcontroller with embedded algorithm. Each real-time identified item is wirelessly and cyclically transmitted to a base station via RF 433.92MHz. As shown in the Figure 2, the MDU is designed to be carried at the waist level nearby close to the center of gravity of the body by means of clips onto the pant belt for easier and convenient use. From the empirical trial, the suitable position lies within the range of 45 degrees from the frontal (antero-posterior) side to either of medio-lateral sides [Bodenheimer et al., 1997].

Figure 1. System structure

Figure 2. The attachment of MDU and the tri-axial orientations of the sensor

The base station in the PTMS is called household distributed data server (DDS), as shown in the Figure 3 The DDS is also a PIC microcontroller-based device that features the capabilities of signal computing, I/O control, Ethernet communication, wireless RF data reception, data storage (MMC) and the links with external devices. Referring to the Figure 1, for data retrieving and management the authorized data administrators (e.g., doctors, care-givers, families) are allowed to access the DDS via the Internet by using the Internet browser or application program. The DDS also features event-driven capability for emergency alarm and report. Immediate delivery of GSM SMS phone messages or e-mail to specific care-givers can be activated when possible fall has been detected.

Figure 3. The household distributed data server (DDS)

2.2 Algorithm design

The algorithm embedded in the PIC microcontroller of the MDU is developed to identify three still postures (lying still, sitting still, and standing still), four postural transitions (sit-stand, stand-to-sit, sit-to-lie and lie-to-sit) and walking movement. Detection for possible falls is also designed in this algorithm. Figure 4 shows the process flow of the algorithm which mainly includes five parts: data sampling (Cx), pre-processing (Px), dynamic posture transition identification (DBx), still posture identification (DAx) and possible fall detection (DCx). All signals are processed in time-domain due to the limited computation capability of the PIC microcontroller and real-time process method. Each item is identified and addressed in 2.5s cycle. In the case where there is no definite result determined throughout the processes, the event will be recorded as an “Uncertain movement” or an “Uncertain posture”.

The data sampling process consists of the primary stage (C1) and the secondary stage (C2) in 0.5s and 2.0s, respectively. The use of the dual-stage data sampling strategy ensures that the data of one event can be acquired within the same sampling interval. Initially, Sections C1 and D1 determine whether any sign of dynamic movement exists. If no dynamic movement be detected (D1=No), the sampled 0.5s data is used to identify one of the three possible still postures in the processes DAx. If dynamic movement is detected (D1=Yes), the secondary data sampling stage (C2) is immediately activated to collect the subsequent 2.0s data. The 2.5s data collected in both stages is combined and then median-filtered (P1, window length n=3) and simplified (P2, one-third scaling by averaging method) to represent a “dynamic event” for the following step-by-step identification. The “slope mapping” technique is commonly used in most processes in this algorithm. This technique registers whether there are apparent changes in the measured data by mapping the analog signals into binary sequence.

Figure 4. The algorithm flowchart

In order to investigate the accelerometric characteristics of sit-stand transitions, a test was performed on 15 ostensibly healthy subjects in various ages arranged in three groups: Young (20-35 yrs), middle-aged (35-50 yrs) and elderly (50+yrs), with 5 subjects in each group. From the observation of the test, the vertical acceleration is used to identify sit-stand postural transitions in which the acceleration patterns can be characterized by three particular rules: (i) peak order, (ii) peak distance (time interval) and (iii) peak values. Either of sit-stand postural transitions can only be identified when the criterion of all the three rules are satisfied. The lie-sit postural transitions can be identified and further distinguished from each other by investigating both the trunk tilt variation and the final posture orientation. Still posture identification requires the information of previously known postural transitions or walking movement. A lying still posture can be recognized according to the posture orientation or if there exists a previous sit-to-lie postural transition. Similarly, sitting still or standing still postures can be identified by the existence of the types of previous sit-stand transitions or walking movement. Walking can be recognized in a regular oscillating form in vertical acceleration. Fall is regarded as a “sign of fall” occurs and is followed by a prolonged lying posture. The sum of tri-axial acceleration values is the determinant for recognizing a “sign of fall”.

For the evaluation of the performance of the algorithm, 10 subjects were recruited for the laboratory-based test. Sensitivity and specificity tests for posture (lying still) and posture transitions (sit-stand transitions, lie-sit transitions) and walking movement were conducted. Note that the evaluation did not include the sitting still or standing still postures due to the fact that both still postures are associated with the results of previously identified postural transitions or movement. In addition, Falling was not included because it was not easy for the testers to simulate “standardized” falls. Table 1 shows the evaluation results of sensitivity and specificity from 200 and 500 samples, respectively.

Table 1. Performance of the algorithm

Posture/activity

Sensitivity (%)

Specificity (%)

Lying still

100

*

Sit-to-stand

92.2

91.5

Stand-to-sit

95.6

88.5

Sit-to-lie

92.2

99.5

Lie-to-sit

95.6

88.0

Walking

98.8

99.5

3.     System integration

The DDS is accessible via the Internet and the real-time status and the counts of each monitored item can be displayed on the Internet browser (e.g., IE ) of the client PC. In addition, a VB-based application program for this system was also developed for complete data management, as shown in the Figure 5. This application program features the following functions:

(1)   Data access via the Internet

The authorized users (e.g., the system administrator, care-giver or families) are allowed to access to the DDS and retrieve the data stored in it through TCP communication.

(2)   Real-time monitoring status

The real-time monitoring information (still posture or dynamic activity) is displayed on this interface.

(3)   Data display

The long-term recorded data in a specific period can be chronologically displayed. Rests and activities can be distinguished and the numbers of counts, percentages of each identified item are shown. All the related information can be saved to an Excel file (*.xls) in the client PC.

(4)   Event-driven function

The DDS can be optionally equipped with a GSM module to provide event-driven capability that can be enabled when a possible fall has been detected. A text message is sent to a user-defined client for fall alarming.

(a)                                                    (b)

Figure. 5 (a) The VB application program for data management. (b) An example of continuous monitoring

A test on continuous ambulatory monitoring at home is conducted with data registered from about 1:00 to 16:00. as shown in Figure 5(b). In the home environment, the test user is not expected to use this system throughout the monitoring period during some situations such as taking a shower, going outside, etc. Therefore, the data recorded may not be constantly continuous, and the actual monitoring time in this test is about 405 minutes, or about 6.75 hours within the span of 15-hour period. Figure 6 is the activity chronograph which chronologically displays the entire recorded events. Lines of different length (also assigned different number) represent respective identified events among the nine item design. A report was made by the subject to register his actual activities under that monitoring period. The monitored data also reveals good correlation with reference to the results from the report.

Figure 6. Example of activity chronograph of the continuous monitored data

4.     Discussion and conclusion

A physical activity monitoring system which utilizes only one wearable sensing device has been developed and demonstrated in ambulatory tests. An advanced tri-axial accelerometer was used in this study to measure the acceleration and trunk tilt of the human body. In fact, the most precise tilt sensing can be maintained when the accelerometer is at static, or under constant acceleration. Tilt sensing using accelerometers still has limited accuracy in changing acceleration magnitude [Elbl, 2005]. However, In spite of this constraint, tilt sensing and acceleration measurement using one tri-axial accelerometer is still valid for physical activity because the resulting outputs still preserve apparent characteristics for either trunk tilt and acceleration patterns.

The wearable MDU has been designed to measure human body movement at waist level and clipped to the belt for minimizing discomfort and inconvenience in use. Power consumption which has significant influence on the effective distance and stability of data delivery is the major issue regarding continuous monitoring. The limitation in computation capability and memory capacity of the microcontroller used in this study, coupled with the fact that human events must be identified simultaneously to keep up with the next data acquisition process, limit the identification performance. Most other off-line systems use powerful PC-based computation software such as MATLAB to analyze the recorded data. Therefore, identification accuracy of those systems is usually higher than that of the real-time systems [9], [13].Despite the fact that the algorithm achieves good performance for laboratory-set tests, those factors may be minimized with advancing progress in microcontrollers and wireless data communication technologies.

In this study, a wearable system for real-time physical activity monitoring using single wearable sensing device was developed for in-home ambulatory monitoring. This system is able to distinguish rests from activities and further identify several target postural transitions and walking. Although the nature of actual human postures and activities of daily living are more complex than what is considered and assumed in the algorithm, this algorithm still exhibits acceptable performance in determining those target postures and activities. Despite some limitations in the configuration for real-time data processing, this system is technically viable to perform long-term ambulatory monitoring in a home environment and to provide sufficient information in evaluating a person’s activities of daily living (ADLs) and his status of physical mobility. In the future, the application field of this system, system robustness and reliability and the possibility for ubiquitous computing which integrates all the ADL-related data altogether should be further considered.

 

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