An intelligent decision supporting system for international classification of functioning, disability, and health
© Hsieh et al.; licensee Springer. 2015
Received: 1 December 2014
Accepted: 27 February 2015
Published: 8 April 2015
In recent years, the population structure in Taiwan has changed so dramatically. Based on concerns of social welfare issues, Taiwanese government began to seek principles for assessment of disability. After seven years of carefully evaluation, the World Health Organization’s International Classification of Functioning, Disability, and Health (Abbreviated to ICF) is officially adopted as Taiwan’s assessment standard while most of the assessment procedures of ICF are sophisticated, and time consuming.
In this paper, we propose a sensor based decision supporting system for ICF. Our prototype system aims to reduce the burden of medical staffs, and to assist subjects to perform the assessments.
This paper integrate multiple devices including ASUS XtionTM, temperature/acceleration/gyro sensors on Arduino, and Zigbee to measure the mobility of limbs and joints. The subject’s log of assessments is then recorded in the database so that the medical staffs can remote-monitor the co ndition of subjects immediately, and analyze the results later. Additionally, in our system, a user-friendly interface is implemented for the detection of dementia.
In this paper, three experiments have been conducted for different purpose. The experiment was conducted to compare the variation between thermometer and our device. Moreover, we invited 20 elders aged for 65 to 80 to use our system and all of them gave positive feedback. Two elders were invited to perform full assessment for dementia and the results show that both of them didn’t have sign of dementia. Also, the assessment of joint movement was performed by a 67 year-old elder and the result shows that the elder had well physical function and could take care of daily life.
The proposed system has potential for aiding users to perform the ICF testing better and provide benefits to medical staffs and society. With current technology, integration between sensor network systems and artificial intelligence approaches will more and more important. We develop a simple interface for user to manipulate and perform the ICF assessment. In addition, the early detection of dementia likely has the potential to provide patients with an increased level of precaution, which may improve quality of life.
KeywordsSocial welfare ICF Arduino Xtion Pro Live™ Sensoring techologies
“People with Disabilities Rights Protection Act” was revised and promulgated in 2007, also new disability classification assessment method and applying disability certification process were scheduled to practice on July 11, 2012 (Directorate-General for the Information Society and Media 2010). People who are physically challenged can apply for the social welfare services according to the results of disabilities assessment.
At the same time, Taiwan is facing the impacts of “aging society with fewer children”, including demographic imbalance, dependency burden, and lack of elderly nursing care services. Based on concerns about these social welfare issues, Taiwanese government began to seek principles for assessment of disability.
After seven years of carefully evaluation, as a result, the World Health Organization’s International Classification of Functioning, Disability, and Health (Abbreviated to ICF) is officially adopted as Taiwan’s assessment standard.
For this new international standard, WHO changed the classification from 16 items in the previous policies to 8 coding system (Directorate-General for the Information Society and Media 2010; World Health Organization 2001; World Health Organization 2007). The old assessment methods and service applying processes are also modified by WHO. One of the most important change in the new policies is the evaluation results are no more available for the entire lifetime. From now on, several aspects of lives have to be investigated at least once every five years.
With these changes, not only the methods that we used to apply social welfare service and attend disabilities assessment are transformed, but also the government is forced to establish new follow up social welfare service, evaluation index, evaluation tool, evaluation flow, etc. Though ICF provides a better system for evaluating disability in accordance with systematic regulations, it takes both people with disabilities and medical staffs more time to perform and complete the evaluation (Liao and Huang 2009; Shotton et al. 2011).
Due to the serious lack of human resource of medicine in Taiwan, in this paper, we propose a sensor based decision supporting system for ICF. At the moment, we focus on the part of assessment for disability and dementia. We developed the friendly interface which leads users to perform the actions required for ICF assessments according to Mobility of joint functions (ID b701). In order to detect users’ movement, this project use Xtion PRO Live to measure and assess subject’s actions and activities; combined with OPENI2 and NiTE2 to conduct the structure of skeleton. We also integrated multiple devices including ASUS Xtion™, Zigbee, and temperature/acceleration/gyro sensors on Arduino as wearable device to capture users’ movement. There are many reports on using Kinect, Xtion PRO LIVE and other related devices for the purpose of motion capture (Mulvenna and Nugent 2010). There are also many software developing tools available for implementing interfaces using these devices. Subject can easily wear our multi-sensors monitoring device on the wrist like wearing a bracelet during the movement assessment test. This wearable device combines temperature, acceleration, and gyro sensors with Arduino. The temperature sensor measures subject’s skin temperature on wrist vein so that inspector can monitor subject’s health state during the test. Besides, the device includes gyroscope to detect the rotations of the wrist for testing items and it can assure that the subject raising their hands in correct orientation. We also implement accelerator to check whether the subject’s movement is smooth or not. If the subject finishes the testing item but with uneven movement, the subject might have potential joint disease. The observation of movement can be used as meaningful information of health.
A dementia assessment interface with gesture recognizer on virtual button is developed to enable users to perform the test under a more convenient and comfortable condition. This project use Webcam combining with OpenCV to develop this interactive GUI interface. We designed the dementia questions according to the items referring to dementia according to Orientation functions (ID b114), Intellectual functions (ID b117), Memory functions (ID b144), and Higher-level cognitive functions (ID 164) in ICF. In this project we assume that subjects all have the basic abilities to answer the question and to recognize words so that subjects can do dementia assessment test by themselves.
For the purpose of releasing the burden of medical staffs and increasing efficiency of evaluation work of ICF, our system collects the subjects’ test results in a database so that the medical staffs can keep up-to-date with subjects’ condition through mobile phones or Internet (Eriksson et al. 2005; Morris 1993).
In the current version of prototype system, we still need a trained volunteer managing the movement assessment assistance interface on the computer to assist subjects to do the test.
As a classification, ICF systematically groups different domains for a person in a given health condition within two parts and each part with two components.
Part one Functioning and Disability has the component (a) Body Functions and Structures, (b) Activities and Participation. Part two Contextual Factors has the component (c) Environmental Factors, (d) Personal Factors.
In this project we implement a method to calculate the degree of joint movement to measure the activity of body movement. As for questions in dementia assessment test are design by us according to part which don’t need medical staffs ask subjects in person from Clinical Dementia Rating (abbreviate CDR).
OpenNI defined 24 joints for a person but the NITE2 offers tracking of 15 of them.
Dementia assessment test
CDR-0.5=very mild dementia
There are six aspects in Clinical Dementia Rating scale. They are memory, orientation, judgment and problem solving, home and hobbies and community affairs (Muilder and Stappers 2009). Moreover, Dementia assessment according to the items in ICF are: Orientation functions (ID b114), Intellectual functions (ID b117), Memory functions (ID b144), and Higher-level cognitive functions (ID 164). We designed the questions referring to above aspects except for home and hobbies and community affairs in Clinical Dementia Rating scale. Caregivers would deal with the questions consisting of the left aspects.
Normal human body temperature depends upon the place in the body at which the measurement is made, and the time of day and level of activity of the person. Temperatures cycle regularly up and down through the day.
0 g offset and sensitivity are factory set and require no external devices. The 0 g offset can be customer calibrated using assigned 0 g registers and g-Select which allows for three kinds of acceleration selection ranges (2 g/4 g/8 g). It is suitable for handheld battery powered electronics because MMA7455L includes a Standby Mode.
A user-friendly interface was developed for the ICF assessment in this paper. The system is based on multi-sensor technology which integrate multiple devices including ASUS XtionTM, temperature/acceleration/gyro sensors. And we invited subjects to perform the assessment through our system. The subject’s log of assessments is then recorded in the database. The log includes subject’s personal information, assessment result and medical history. Our system is likely to extract useful information for medical staffs and support decision making. Written informed consent was obtained for the publication of this report and any accompanying images.
The first process of our system is joints mobility measurement. In the beginning of the joints mobility measurement, in order to let Xtion PRO LIVE detect the subject successfully, the subject needs to stand in the restricted position. After detecting successfully, the subject can start the measurement. In addition, during the assessment, the subject is asked to wear our sensing device on his/her wrist for detecting subject’s body temperature. There are three sensors installed on this device, including a body temperature sensor, an accelerator sensor, and a gyroscope sensor. After the subject completes a designated posture, the inspector can keep going on next movement. After all testing movements are completed, the results are then recorded into the database.
As for dementia progression assessment, our system picks out 10 questions from 100 designed questions in the database randomly. Our system implement a multimodal interface for subjects to input their answers either by using a traditional keyboard-mouse combination, or by using gestures directly with virtual push-buttons on screen. The reason why we implemented two methods for users to manipulate the system is because we are afraid that the elder are unable to control the mouse properly. Instead of mouse, we think manipulate by gesture is a more instinct way for the elder.
Finally, Medical staffs can query and check the assessment results through their mobile devices or computers by requesting the database.
Joints mobility measurement
Joints mobility measurement comprises two parts as following: upper extremity and lower extremity.
According to the items from Mobility of joint functions (ID b701) in ICF, we pick out the relative measurement testing items as below:
Mobility of shoulder
Range of motion at the shoulder extension
(Standard degree: 180)
Range of motion at the shoulder flexion
(Standard degree: 60)
Joint function (standard degree: 240).
Mobility of elbow
Range of motion at the elbow extension
(Standard degree: 145)
Range of motion at the elbow flexion
(Standard degree: 0)
Joint function (standard degree: 145)
Mobility of wrist
Range of motion at the wrist extension
(Standard degree: 80)
Range of motion at the wrist flexion
(Standard degree: 70)
Joint function (standard degree: 150)
Mobility of hip
Range of motion at the hip extension
(Standard degree: 125)
Range of motion at the hip flexion
(Standard degree: 10)
Joint function (standard degree: 135)
Mobility of knee
Range of motion at the knee extension
(Standard degree: 145)
Range of motion at the knee flexion
(Standard degree: 0)
Joint function (standard degree: 140)
The joints mobility measurement result of 67 year-old subject
Range of motion
Dementia assessment interface
In order to improve the accuracy of our system, full examination was performed by two elders aged 67 and 77 respectively. And their family, as caregiver, was asked to answer the AD8 questionnaire which is the version for caregiver. All the questions are based on daily life, for example, the color judgment, shape recognition, and simple calculation. We couldn’t determine if the subject have dementia by merely one test. The assessment factor should consist of assessment test, the questionnaire for caregiver and the judgment by medical staffs. The main purpose of our system is preliminary assessment and it’s clearer for medical staff to understand subject’s situation. The standard of high risk population is that if answer’s correctness percentage is lower 80% and the questionnaire answered by caregiver got low score, the subject need to be kept under observation.
As previously mentioned, we integrated temperature/acceleration/gyro sensors on Arduino which we developed the device with open source Arduino software. We implemented Zigbee to detect users’ body temperature and sophisticated movement during the test. The detail prototype would be described in this session respectively. For the convenience of measurement, these devices were integrated into a sensing module for users to wear on the wrist so that won’t cause their uneasiness.
Temperature sensing device
In this paper, in order to observe the sophisticated movement of wrist, we use L3G4200D to detect the rotation of wrist. And moreover, to observe if the subject’s movement is fluently or not, our system use MMA7455L as the sensor to detect the rate of subject’s movement.
Query interface on the mobile and on the computer
We developed the query interface on the mobile and on the computer which can connect to the database and access the assessment result so that medical staffs can query the results immediately. To enter the system, medical staffs need to create an account. After entering the system, if subjects’ information has not been established yet, medical staffs need to create subjects’ basic information for the sake of convenience querying. Then the assessment can start after establishing this information.
In the joint mobility measurement, subject was able to perform the measurement according to the demonstration animation and the degree of joint mobility was recorded in database. Through the subject’s moment we could observe not only if he/she has the ability to take care of themselves but also the inconvenience might occur in everyday life. The experiment results show that our system has potential for enabling the ICF assessment to be performed by users themselves or with assistance from family, and therefore releasing medical staffs’ burden.
Our research results contain the following three parts: First, our system can successfully track user’s movement. And consequently, most of the measurement results, which was originally performed by subject with the assistant of medical staffs, can now be recorded automatically under the help of volunteer. Therefore our system is able to release medical staffs’ burden. Second, our system can measure and visualize user’s body temperature automatically. In addition, through our graphical interface, helpers and medical staffs can acknowledge auxiliary information of subject’s body condition more easily than the conventional way. Third, our system provides a user-friendly multimodal interface for elders, such as the virtual buttons with webcam-based gesture recognizer, etc.
For the current version, we still have lots of work to do. In the part of joint mobility measurement, we are adding a voice guide to encourage users while performing assessment. Also, we are also implementing a wire-less sensor network so that we can overcome some measuring problems for sophisticated joints, such as ankle and wrist.
Comparison of the joint mobility measurement results of the system and those done by an expert as gold standard is suggested for validation. We may need a lot of subjects to have the statistics of P-values. The questions about dementia are even more complicated. We may need comparison between the answers by subjects using computer and the answers by the expert. We are planning to test our system on more patients with the help of medical staffs in the near future. Finally, being encouraged by the positive feedbacks from both medical staffs and elders, we are also planning to further develop our system as a more general platform for visualizing medical metadata.
In this paper, we described an ongoing project of a sensor-based decision supporting system for ICF. Many parts of this project are still at its early stage. However, the first goal which aims to develop a conceptual prototype by utilizing the advices from medical staffs, is reported in this paper. And the requirements for the research of this paper in the medical field are higher and higher.
- Directorate-General for the Information Society and Media. (2010). Advancing and applying Living Lab methodologies: an update on Living Labs for user-driven open innovation in the ICT domain. European Union: European Network of Living Labs publication.Google Scholar
- Eriksson, M, Niitamo, V-P, & Kulkki, S. (2005). State-of-the-art in utilizing Living Labs approach to user-centric ICT innovation–a European approach. Technology 13, 1, 1–13.Google Scholar
- Liao, H-F, & Huang, A-W. (2009). Introduction to International Classification of Functioning, Disability and Health (ICF) and recommendations for its application in disability evaluations in Taiwan. Formosan Journal of Physical Therapy, 34(5), 310–318.Google Scholar
- Morris, JC. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.View ArticleGoogle Scholar
- Muilder, I, & Stappers, PJ. (2009). Co-creating in practice: result and challenges, collaborative innovation: emerging technologies, environments and communities (pp. 22–24).Google Scholar
- Mulvenna, MD, & Nugent, CD. (2010). Supporting people with dementia using pervasive health technologies. Berlin: Springer.View ArticleGoogle Scholar
- Reed, G, Bufka, L. (2006). Using the ICF in Clinical Practice, Annual North American Collaborating Center Conference on ICF; 12, [Online] http://www.wcpt.org/sites/wcpt.org/files/files/KN-ICF-Clinical_practice.pdf.
- Shotton, J, Fitzgibbon, A, Cook, M, Sharp, T, Finocchio, M, & Moore, R. (2011). Real-time human pose recognition in parts from single depth images. Computer Vision and Pattern Recognition (CVPR), 2, 1297–1304.Google Scholar
- World Health Organization. (2001). International classification of functioning, disability and health: ICF-CY. Geneva: World Health Organization.Google Scholar
- World Health Organization. (2007). International classification of functioning, disability and health: children and youth version: ICF-CY. World Health Organization.Google Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.