//Logo Image
Author: Che-Chang Yang(2006-09-01); recommendation: Yeh-Liang Hsu (2006-09-01).
Note: This article is Chapter 1 of Che-Chang Yang’s Master thesis “Development of a Portable System for Physical Activity Assessment in a Home Environment.”

Chapter 1. Introduction

1.1     Motivation of the research

1.1.1 Assessment of physical activity

Physical activity can be regarded as any 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. The 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 [Foerster et al., 1999].

The assessment of physical activity is difficult due to the subtle and complex nature of body movement which requires precise and reliable measuring techniques. Standard human motion capturing techniques based on optical, magnetic and ultrasonic systems allow a complete kinematic analysis but require a dedicated laboratory [Aminian et al., 2004]. In a magnetic motion capturing system, the body positions and joint rotations can be measured by several sensors placed on the body of the subjects in a magnetic field [Bodenheimer et al., 1997]. Dickstein et al. [1996] presented the application of a kinematic measurement system based on ultrasonic technique to assess stance balance in a clinic. As for the optical sensing technique, subjects are asked to place several passive reflective markers or active markers (e.g. LED, infrared) on different body parts while cameras are installed around the interior of the room to capture their motion.

From a technological point of view, these techniques mentioned above can precisely capture human motions and have been used in the applications of computer animation and virtual reality. However, for the assessment of daily physical activity in a home environment the cost of such techniques is unacceptable for common use. Moreover, the subjects must be restrained inside a laboratory-like space, which is entirely different from a free-living home environment. Therefore, acquisition of body movement using portable measuring devices or body-fixed motion sensors is an appropriate alternative for physical activity assessment in ambulation and home environments.

In the past, mechanical motion sensors have been used for various physical activity assessments. Saris [1977] used pedometers and actometers to study daily physical activity. Energy expenditure related to various physical activities has also been estimated using this technology [Verschuur et al., 1980]. Meanwhile, the use of accelerometers to measure body movement began as early as the 1970s. Morris [1973] developed an accelerometry-based technique to measure human body movement. Meijier et al., [1991] proposed a method to assess physical activity with motion sensors and accelerometers. They also developed a small data acquisition unit with a solid state memory in place of a large tape recorder which discouraged subjects from wearing it. Veltink et al. [1996] investigated the feasibility of distinguishing several static and dynamic activities (standing, sitting, lying, walking, ascending stairs and descending stairs) in a domestic environment using a small set of two or three uniaxial accelerometers. Bouten et al. [1997] also presented a tri-axial accelerometer which consists of three separate orthogonal uniaxial accelerometers to measure body movement.

Najafi et al. [2003] developed a portable data processing unit for the off-line acquisition, processing, and storage of the acceleration data. An ambulatory system was presented for daily physical activity monitoring of the elderly using a kinematic sensor which consists of a miniature gyroscope and two dual-axial accelerometers. The angular velocity of the trunk, accelerations from the trunk and frontal-trunk were all measured for body motion analysis. Wavelet transformation, in conjunction with a simple kinematics model, was used to detect various postural transitions and walking periods during daily physical activity. In addition, fall risk evaluation was proposed. Aminian et al. [2002] also developed an off-line ambulatory gait analysis system. In this system, as shown in Figure 1.1, four miniature gyroscopes are attached respectively to each shank and each thigh for angular rotation measurement. Sensing data are stored in a portable data logger (Physilog system). With this configuration, the lower limb movement during walking can be measured.

Figure 1.1 Measurement configuration of a gait analysis system [Aminian, 2002]

1.1.2 Tele-homecare Systems

According to the UN’s definition, a society in which more than 7 percent of the total population is aged 65 or over is referred to as an aging society. Taiwan has become an aging society since 1993. Currently in 2005 the population aged 65 or older is over 2 million (9.74%). The needs of health care and management for the elderly has become an important issue. The increasing needs for elderly care cannot be solved by merely increasing the number of care-givers.

The home has become the centerpiece of health delivery systems today. Tele-homecare can be defined as the use of information and communication technologies to enable effective delivery and management of health services at a patient’s residence [Office of Health and Information Highway, Canada, 1998]. Tele-homecare differs from tele-medicine in the sense that people who transmit and receive medical information are not necessarily medical doctors but the patients themselves and their families, nurses, care-givers, home-helpers and medical technical experts [Tsuji, 2002]. Koch [2006] gave an overview about the current state and future trends in research on home telehealth in an international perspective.

The Portable Tele-homecare Monitoring System (PTMS) has currently been under development by the Gerontechnology Research Center, at Yuan Ze University. Instead of using a centralized database structure that gathers data from many households, in PTMS a household is the fundamental unit for data transmission, storage, and analysis. Equipped with different sensors, PTMS can be used for long-term personal health data management of the elderly in a home environment. PTMS also provides care-givers with convenient access to the health data of the elderly, and real time event-driven messages in urgent situations. Several PTMS applications are demonstrated, including environment and daily behavior monitoring, an RFID-based entrance guard system, sleep quality monitoring, vital sign monitoring, and a tele-presence robot.

1.1.3 Purpose of the Research

The purpose of this research is to develop a wearable system for physical activity assessment based upon the PTMS infrastructure. Figure 1.2 shows the structure of the system. A tri-axial accelerometer is used for motion detection of body movement. A dedicated body movement algorithm embedded in the microcontrollers is developed to actively recognize human still postures (sitting, standing and  lying), posture transitions (sit-stand type or lie-sit type) and locomotion (walking) in a home environment. The tri-axial accelerometer and the microcontroller are integrated with a wireless communication module to form a wearable data acquisition unit (DAU), which transmits the signals of body movements to the distributed data server (DDS), which is the core of the PTMS.

The DDS wirelessly receives the identified signals of body movements from the wearable data acquisition unit in real time. These events are classified and then stored in the Multi-Media Card (MMC). Users can access the DDS using an Internet browser or a dedicated VB program to retrieve historical data and perform data management. Quantitative assessments of one’s health condition such as a vitality index, regular routines, daily behaviors and senile dementia will be reported by analyzing the long-term historical data. With the event-driven capabilities of the DDS, caregivers are informed automatically of any possible emergencies by cell phone text message or E-mail.

Figure 1.2 System structure

1.2     Structure of the Thesis

Chapter 2 of the thesis reviews the accelerometry for human body movements. Related literature referring to motion sensors, sensor attachment and measurement techniques are discussed. Preliminary description of acceleration patterns including human postures, posture transitions and locomotion is demonstrated.

Chapter 3 describes the design of the newly developed 3rd generation DDS. In Chapter 4, the design of a portable data acquisition unit composed of a tri-axial accelerometer, microcontrollers and an RF wireless transmission module will be presented. An algorithm for body movement recognition and a method for quantitative assessment are discussed in Chapter 5. System integration and ambulatory tests in a residential space will also be reported in this chapter 6.

Chapter 7 concludes this study. Possible extensions of this research, for example, Physical activity assessment with ubiquitous computation network in existing PTMS infrastructure is discussed, is also discussed.


Aminian, K., Najafi, B., 2004. “Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications,” Comp. Anim. Virtual Worlds, pp. 79-94, John Wiley & Sons.

Aminian, K., Najafi, B., Bula, C., Leyvraz PF., Robert, Ph., 2002. “Spatial-temporal parameters of gait measured by an ambulatory system using miniature gyroscope.” Journal of Biomechanics, vol. 35, pp. 689-699. 

Bodenheimer, B., Rose, C., Thalmann, D., 1997. “The process of motion capture: dealing with the data,” Computer Animation and Simulation’97.

Bouten, C. V. C., Koekkoek, K. T. M., Verduin, M., Kodde, R., Janssen, J. D., 1997. “A Triaxial Accelerometer and Portable Data Processing Unit for the Assessment of Daily Physical Activity,” IEEE Trans. Biomed. Eng., vol. 44, pp.136-147.

Capspersen, C. J., Powell, K. E., Christenson, G. M., 1985. “Physical activity, exercise and physical fitness: Definitions and distinctions for health related research,” Public Health Rep., vol. 110, pp. 126-131.

Dickstein, R., Abulaffio, N., Gelernter, I., Pillar, T., 1996. “An ultrasonic-operated kinematic measurement system for assessment of stance balance in the clinic,” Clinical Biomechanics, vol. 11, pp. 173-175.

Foerster, F., Smeja, M., and Fahrenberg, J., 1999. “Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring,” Comput. Hum. Beh., vol. 15, pp. 571-589.

Koch, S., 2006. “Home telehealth – Current State and Future Trends,” International Journal of Medical Informatics, article in press.

Meijier, G. A. L., Westerterp, K. R., Verhoeven, F. M., Koper, H. B. M., Hoor, F. T., 1991. “Methods to assess physical activity with special reference to motion sensors and accelerometers,” IEEE Biomed. Eng., vol. 38, pp. 221-229,

Moris, J. R., 1973. “Accelerometry-a technique for the measurement of human body movements,” J. Biomech., vol. 6, pp. 729-736.

Najafi, B., Aminian, K., 2003. “Measurement of Stand-Sit and Sit-Stand Transitions Using a Miniature Gyroscope and Its Application in Fall Risk Evaluation in the Elderly,” IEEE Trans. Biomed. Eng., vol. 49, pp. 843-851.

Office of Health and Information Highway, 1998. “International Activities in Tele-homecare”, Health Canada.

Saris, W. H. M. and Binkhorst, R. A., 1977. “The use of pedometer and actometer in studying daily physical activity in man. Part I: reliability of pedometer and actometer,” Eur. J. Appl. Physiol., vol. 37, pp. 219-228.

Saris, W. H. M., Binkhorst, R. A., 1977. “The use of pedometer and actometer in studying daily physical activity in man. Part II: validity measuring the daily physical activity,” Eur. J. Appl. Physiol., vol. 37, pp. 229-235.

Tsuji, M., 2002. “The Telehomecare/Telehealth System in Japan,” WMA-Business Briefing: Global Health 2002, pp. 72-74.

Verschuur, R., Kemper, H. C. G., 1980. “Adjustment of pedometers to make them more valid in assessing running,” Int. J. Sports. Med., vol. 1, pp. 95-97.

Veltink, P. H., Bussmann, H. B. J., Vries, W. D., Martens, W. L. J., Lummel, R. C. V., 1996. “Detection of static and dynamic activities using uniaxial accelerometers,” IEEE Trans. Rehab. Eng., vol. 4, pp. 375-385.