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Author: Che-Chang Yang (2006-09-01); recommended: Yeh-Liang Hsu (2006-09-01).
Note: This article is Chapter 7 of Che-Chang Yang’s Master thesis “Development of a Portable System for Physical Activity Assessment in a Home Environment.”

Chapter 7. Discussion and conclusion

In this study, a system for human physical activity assessment using only one portable sensing device was developed for real-time ambulatory monitoring in a home environment. This system is able to distinguish rests from activities and further identify several target posture transitions and movements. 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 (ADL) and his status of physical mobility.

7.1     System limitations

The physical activity assessment system which utilizes only one wearable sensing device has been developed and demonstrated in ambulatory tests. This system also achieves good performance in still posture and dynamic activity identification. However, some inherent limitations are worth discussing here.

(1)      Tri-axial accelerometer

An advanced capacitance 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. However, tilt sensing using accelerometers still has limited performance in accuracy. Degradation of tilt sensing accuracy in changing acceleration magnitude is an inevitable problem. For example, Equation 7.1 is a formula for this accelerometer to calculate tilt angle relative to the Y axis when rotating about the X axis. This equation is a function of the three acceleration components (aX, aY and aZ). Consider a case, for example, where the accelerometer is moving upward (positive Z direction) without any rotation (tilt). Obviously, there must be no change except the Z axis acceleration component aZ. However, an unreasonable tilt angle will be obtained for such a case according to this equation.

                                                                                   (7.1)

This example explains a possible paradox probably encountered in empirical uses. In Elble’s report on gravitational artifact in accelerometric measurement [2005], it was stated that the interference of gravitational artifact cannot be solved using either bi-axial or tri-axial accelerometers. Therefore, the acceleration data acquired in this study cannot be used to precisely interpret the complex nature of human motions. 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.

(2)      The wearable DAU

The wearable DAU has been designed to measure human body movement. It is designed to be clipped onto a waist belt for minimizing discomfort and inconvenience in use. However, carrying the DAU might limit posture and movement when lying down and therefore further influences the subject’s comfort.

As for wireless data transmission, power capacity has a significant influence on the effective distance and stability of data delivery. In low power status, the transmitter fails to function properly whereas the other parts, such as PIC microcontroller and accelerometer still function normally. To extend the time for use, a battery cartridge with (3×AA alkaline batteries) can be used instead of the AAA batteries. The antenna configuration has a great influence on the performance of wireless data delivery, as well. However, the antenna design has not been further evaluated. In the future, the onboard antenna and optimization must be taken into consideration.

(3)      Real-time identification

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 [Karantonis, 2006].

Moreover, the durations of posture transitions or movement are not the same each time, even for the same person. People usually perform mixed and combined movements in their normal activities of living. Due to the complex nature of human movement and limitations in instrumentation, identification accuracy for such a real-time system can be limited when applied in real ambulatory and home uses, despite that fact that it achieves good performance for laboratory-set tests.

7.2 Future work

The real-time system for physical activity identification and assessment has been technically proven to be feasible. The results from the ambulatory tests also show that this system can provide significant information on the subject’s activities of daily living. In the future, there are still some improvements and potential developments to be considered:

(1)   Application fields of the system needs to be identified

The system developed in this study may find applications in various fields, such as elderly care at home or in nursing homes, physical rehabilitation, etc. The application fields and the usefulness of this system need to be identified and justified, and the system may need to be adjusted for different application fields.

(2)   Further improvement in instrumentation

The current system uses conventional 433.92MHz RF transmission module. However, the ZigBee wireless communication module in IEEE 802.15.4 application is a better alternative for its advancement in data “hopping” capability and low power consumption. It is also possible to miniaturize the device, both the wearable DAU and the DDS, towards a more commercialized level product design. In addition, human factors also need to be considered, for example, the device attachment design, etc.

(3)   System robustness, reliability and performance ambulatory evaluation

To completely validate the robustness and reliability of the system and the performance of the algorithm, this study still requires more information from large-scale ambulatory evaluation in home environments. For the potential target user group, e.g. older adults, their responses to the use of the system as well as the identification accuracy needs to be further investigated.

To further enhance accuracy, a technique such as rule-based event classification can be employed [Mathie et al., 2004]. In addition, it was observed that one set of parameters in the algorithm were not applicable to all the users with different mobility capabilities. Therefore, multi-mode parameter functions in the algorithm should be implemented to fit the users with various mobility characteristics.

(4)   Toward a ubiquitous computing system

Physical activity assessment is one branch of the human ADL-related research field. We should further investigate the possibility to integrate the information of environmental sensors (e.g., temperature, humidity, luminance, etc.), space-embedded activity sensors and vital sign devices (sphygmomanometer, glucose-meter, weight scale, etc.) together to establish the platform of a ubiquitous computing system.

Reference

Elble, J. Rodger, 2005. “Gravitational artifact in accelerometric measurements of tremour,” Clinical Neurophysiology, vol. 116, pp. 1638-1643.

Karantonis, Dean M., Narayanan, Michael R., Mathie, Merryn., Lovell, Nigel H., Celler, Branko G., 2006. “Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring,” IEEE Trans. Info. Tech Biomed., vol. 10, No. 1, pp. 156-167.

Mathie, M. J., Celler, B. G., Coster, A. C. F., Lovell, N. H., 2004. “Classification of basic daily movements using a triaxial accelerometer,” Medical & Biological Engineering & Computing, vol. 42, pp. 679-687.