Motion Recognition for Kinect Sensor Data Using Machine Learning Algorithm with PNF Patterns of Upper Extremities

다국어 초록 (Multilingual Abstract)

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown.
Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed.
The motion data for wearing a gown were used for the test set.
Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF.
Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion.

참고문헌 (Reference)

1 임재길, "스프린터와 스케이터를 이용한 고유수용성촉진법 패턴운동이 뇌졸중 환자의 균형 및 보행 기능에 미치는 효과" 대한물리치료학회 26 (26): 249-256, 2014

2 김지호, "기울기와 위치 정보를 이용한 손동작기반 실시간 숫자 인식기 구현" 한국정보처리학회 2 (2): 199-204, 2013

3 Clark RA, "Validity of the microsoft kinect for assessment of postural control" 36 (36): 372-377, 2012

4 Bonnechere B, "Validity and reliability of the kinect within functional assessment activities : Comparison with standard stereophotogrammetry" 39 (39): 593-598, 2014

5 Vikramkumar, B V, "Trilochan. Bayes and naive bayes classifier" 1404 : 933-, 2014

6 Windolf M, "Systematic accuracy and precision analysis of video motion capturing systems--exemplified on the vicon-460 system" 41 (41): 2776-2780, 2008

7 이경미, "SVM을 이용한 동적 동작인식 : 체감형 동화에 적용" 한국콘텐츠학회 13 (13): 64-72, 2013

8 Son J, "Rule selection method in decision tree models" 2013 (2013): 13-23, 2013

9 Yang Y, "Reliability and validity of kinect rgb-d sensor for assessing standing balance" 14 (14): 1633-1638, 2014

10 정동훈, "Reliability and Validity of the CAP for Computer Access Assessment of Persons with Physical Disabilities" 대한물리치료학회 27 (27): 30-37, 2015

11 Shotton J, "Real-time human pose recognition in parts from single depth images" 1297-1304, 2011

12 Rohr K., "Incremental recognition of pedestrians from image sequences. Computer Vision and Pattern Recognition, 1993" 8-13, 1993

13 Della Croce U, "Human movement analysis using stereophotogrammetry. Part 4: Assessment of anatomical landmark misplacement and its effects on joint kinematics" 21 (21): 226-237, 2005

14 Zhou H, "Human motion tracking for rehabilitation—a survey" 3 (3): 1-18, 2008

15 Sin BK, "Hmm toolbox development and analysis and comparison of hmm types" 2010 (2010): 9-10, 2010

16 이근호, "Effects of Virtual Reality Exercise Program on Balance in Multiple Sclerosis Patients" 대한물리치료학회 27 (27): 61-67, 2015

17 박성두, "Comparison of Balance and Fall Eff icacy of Virtual Reality Program in Elderly Women with Fall Experience" 대한물리치료학회 26 (26): 430-435, 2014

18 Hwang SS, "Basic concept of pnf and its approach for sports injury" 2002 (2002): 140-143, 2002

19 Rabiner L, "A tutorial on hidden markov models and selected applications in speech recognition" 77 (77): 257-286, 1989

20 Chang YJ, "A kinect-based system for physical rehabilitation : A pilot study for young adults with motor disabilities" 32 (32): 2566-2570, 2011

21 Pastor I, "A feasibility study of an upper limb rehabilitation system using kinect and computer games" 1286-1289, 2012