Tangwongcharoen, Wisan
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Item type:Publication, The specific golf swing patterns using K-means clustering with the two-sided confidence interval(2017-11-03); Titiroongruang, WisutGolf 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 K-Means Clustering with Two-Sided Confidence Intervals and the Closest Pair of Points Problem. The raw data were clustered by K-Means Clustering. The boundaries of subgroups processed by K-Means Clustering were calculated to represent the data of the normal and abnormal golfers, which we use as the diagnose patterns. The compared data is the comparison of the diagnose patterns of both normal and abnormal patterns. From the experimental results, the percentage of similar pattern is shown. Therefore, this algorithm can help the doctor to predict the injuries trend of golf players. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Determining Golf Swing Patterns Using Motion Sensors for Injury Prevention(2017-01-01); Titiroongruang, WisutGolf 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Define Stance and Swing pattern of gait cycle using motion sensor and K-Mean Clustering(2022-01-01); This research studied walking patterns based on the gait cycle focused on the stance and swing period. We are interested in creating the pattern for representing a normal person and person with a walking disorder by distinguishing patterns. This research uses Razor-IMU to collect all walking data and transfer data from sensors via WIFI which helps gain data to be stable and accurate.After collecting the walking data, we transformed data into linear graphs to reference the gait cycle pattern. Because the graph in linear form can show the movement and distinguish between normal and abnormal people the difference. The aim is to obtain representative data of normal and abnormal people for further analysis. Therefore, the data were then grouped using K-mean Clustering. The data obtained from the clusters were able to distinguish between normal and abnormal walking distances. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Development of Knee Support for Vastus Medialis Oblique Muscles for Thai Patients(2020-06-01); Churdchomjan, WorachartThis study proposed the design of knee training and equipment for the Thai people physiology. This equipment allows the patient to fully control the stretching of the knee, the Vastus Medialis Oblique (VMO) movement fully extended. The researchers developed a knee support training device using an IMU sensor attach to the knee and sent knee extension exercise data to the tablet via Wi-Fi. The training program support 3 postures; sit and move the leg forward, stand up and move the leg backward, stand up and move the leg beside. Training provides 4 games to simulate a knee extension. Trainees control knee movement to control the direction of the game. The experimental result was gaining from 70 participants who are aging 18-45 all genders, with VMO weakness, Testers was divided into 2 groups: did only exercise, exercise using Trigo wireless. Electromyography (EMG) is a measure of the level of knee stretching at the end of the movement of VMO and Vastus Lateralis (VL), which used in exercise for the treatment of knee pain. The evaluation based on the patient's stretching ability and the practice. The results of tester who using the training tools have improvement. The ability to flex the knee can be increasing by an average of 36° (in the range 0° - 90°) and the average pain level was decreasing by 3.1 levels (in the range of 0 - 10 levels). From the experimental results, the development of training as a model for the development of a knee support training program prevented injury in the elderly and muscle recovery from deterioration and injury.