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    Design and Development of a Prototype for Measuring Range of Motion
    (2018-07-02)
    Jongprasithporn, Manutchanok
    ;
    Yodpijit, Nantakrit
    ;
    Chanaroon, Thachamaporn
    ;
    Paiboonrattanakorn, Thunjira
    ;
    Sittiwanchai, Teppakorn
    The ability to perform range of motion can help human maintain mobility and flexibility. The transitional device for measuring range of motion is Goniometer, a manual method. This method is depended on users' experience and skill which could lead to wrong interpretation and results. The purpose of this project is to design and develop a prototype for measuring Range of Motion (ROM) by using Inertial Measurement Units (IMUs). The prototype, called ROMie, is including hardware and software. ROMie had been validated for accuracy and sensitivity at 45, 60, 90 and 180 degrees. ROMie was tested for forty times at each angle,. The results showed that ROMie has the overall accuracy greater than 95 percent. Sensitivity is equal 1 at 95 percent confidence interval and greater than 0.875 at 99 percent confidence interval.
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    The use of smartphone for gait analysis
    (2017-06-07)
    Yodpijit, Nantakrit
    ;
    Tavichaiyuth, Nicha
    ;
    Jongprasithporn, Manutchanok
    ;
    Songwongamarit, Chalida
    ;
    Sittiwanchai, Teppakorn
    In today's information-rich environment, one of the most popular devices is a smartphone. Research has shown significant growth in the use of smartphones and apps all over the world. Accelerometer within smartphone is a motion sensor that can be used to detect human movements. Compared to other major vital signs, gait characteristics represent general health status, and can be determined using smartphones. The objective of the current study is to design and develop the alternative technology that can potentially predict health status and reduce healthcare cost. This study uses a smartphone as a wireless accelerometer for quantifying human motion characteristics from four steps of the system design and development (data acquisition operation, feature extraction algorithm, classifier design, and decision making strategy). Findings indicate that it is possible to extract features from a smartphone's accelerometer using a peak detection algorithm. Gait characteristics obtain from the peak detection algorithm include stride time, stance time, swing time and cadence. Applications and limitations of this study are also discussed.
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    Determining Golf Swing Patterns Using Motion Sensors for Injury Prevention
    (2017-01-01)
    Tangwongcharoen, Wisan
    ;
    Titiroongruang, Wisut
    Golf is a popular sport for exercise or socializing. It affects an increasing number of patients. Because of these reasons the researchers decided to focus on this problem. We presented the analysis golf swing using sensors named Razor IMU to detect golf swing motions. The rotation and acceleration data were gathered by sensors attached on the upper and lower back. These data were clustered by K-Mean Clustering. The data clusters were calculated boundaries by Z-Score. The normal and abnormal data were compared for the Back Swing-Half Swing to Top Swing position and Top Swing to impact position. From the experimental results, this algorithm can classify normal and abnormal data due to the significant differences. This paper can help to improve and correct swings and thus avoid injuries.