RELIABILITY OF WALKING SPEED, STRIDE TIME AND STRIDE LENGTH VARIABILITY USING FEEDBACK-CONTROLLED TREADMILL

The purpose of this study was to evaluate the test-retest reliability of walking speed, stride time and stride length variability using a feedback-controlled treadmill. Ten subjects performed 3 trials of walking experiment for 5 min with a feedback-controlled treadmill. To check the reliability of gait parameters such as walking speed, average and variability (i.e. coefficient of variance, CV) of stride time and stride length, those parameters were compared in this study. Results showed that all gait variables were confirmed the reliability within-trial (walking speed: intra-class correlation, ICC=0.996, standard error of measurement, SEM=0.03, average stride time: ICC=0.989, SEM=0.01, stride time variability: ICC=0.680, SEM=0.18, average stride length: ICC=9.994, SEM=0.01, stride length variability: ICC=0.830, SEM=0.25). This feedback-controlled treadmill can be used for various gait dynamics study using spatio-temporal variables

IDENTIFYING CRITICAL KINEMATIC PARAMETERS FOR BETTER GOLF PUTTING

In modern golf competition, putting is one of the crucial parts of the game. It has been reported that putting accounts for about 40% of all golf shot played in tournaments (Gwyn & Patch, 1993). Wiren (1992) also indicated that, on average, putting constitutes 38% of all golf strokes in competition and improving putting skills is th e fastest way to lower the score. However, it is also true that most recreational golfers neglect the putting and seldom practice it hard. Despite this revealing statistics and the obvious importance of competent putting, much of the pedagogical literature is based on the observations and anecdotal evidence provided by top players and coaches (Paradisis & Rees, 2004). Therefore, the purpose of this study was to identify critical kinematic parameters of a putt by comparing putts performed by elite and novice golifers, and nongolfers. The findings might provide valuable information for improving putting performance

A STUDY ON THE LONG-TERM MONITORING OF SPORTS ACTIVITIES

INTRODUCTION: Lots of studies to analyze and classify human movement patterns using various sensors have been carried out (Mathie, 2004; Allen, 2006) because accurate information of body activity is required to provide promotion of health and health plan. Thus this study was conducted to study the classification and monitoring of various sports activities in real-time environment using single waist mounted tri-axial accelerometer

CHANGES IN MUSCLE ACTIVITY PATTERN IN LOWER LIMB EXTREMITY DURING PEDALLING BY SADDLE HEIGHT

As a preliminary study for a preferential saddle height for cyclist, muscle activity pattern in lower limb extremity between preferred and higher saddle height during pedalling was compared. Seven cyclists performed 3 min sub-maximal pedalling under two different saddle heights. Range of motion (ROM) of muscle length, onset-timing of muscle activity, and amount of muscle activity in biceps femoris (BF), vastus lateralis (VL), gastrocnemius (GM), and tibialis anterior muscles (TA) were used to compare changes in muscle activity pattern due to saddle height. Results showed that there was a significant difference in ROM of VL and that of TA (

A STUDY ON THE GRIP FORCE DURING PUTTING STROKE

There are lots of variables to affect the control of ball movement during golf putting. Among several variables, it is believed that grip force during putting stroke is one of the important variables. However, there is not much quantitative evidence from published literature (Delay 1997, Gwyn 1993). Therefore, the purpose of this study was to quantify the grip force by comparing putts performed by elite and novice golfers and to identify the relationship between kinematic parameters and the grip force at 16 different parts of subjects’ right and left hand at each putting phase

Effects of Simultaneous Cognitive Task on Gait Event Accuracy with Auditory Stimuli: Comparison between Young Adults in Their 20s and the Elderly in their 70s

The purpose of this study was to compare the difference in the accuracy of gait events between young and older adults during metronomic walking by auditory cueing. Additionally, age-specific changes in the gait event accuracy according to additional simultaneous cognitive tasks were examined. The time interval (or temporal error) between the auditory cue (i.e., metronome) and the heel contact was used as the accuracy of the gait event. Fifteen young group (YG, 24.7 ± 0.8 years) and 14 elderly (EG, 78.4 ± 5.5 years) people participated in the experiment. The temporal errors under two gait conditions (MET: walking with metronome; MET + BC: walking with metronome while counting backward) were compared for each group. The results revealed that all the temporal errors of EG were significantly greater than those of YG. While the addition of simultaneous cognitive tasks resulted in a significant increase in temporal error in both age groups, the coefficient of variation (CV) of the temporal error significantly increased only in the EG group. In other words, although heel contact accuracy with auditory stimuli was affected by the simultaneous cognitive task in both groups, it was demonstrated that the variability of the error in the young adults remained constant. Therefore, the time error measurement used in this study has the potential to be used as a tool to judge the gait instability of the elderly compared with young adults

Effects of Simultaneous Cognitive Task on Gait Event Accuracy with Auditory Stimuli: Comparison between Young Adults in Their 20s and the Elderly in their 70s

The purpose of this study was to compare the difference in the accuracy of gait events between young and older adults during metronomic walking by auditory cueing. Additionally, age-specific changes in the gait event accuracy according to additional simultaneous cognitive tasks were examined. The time interval (or temporal error) between the auditory cue (i.e., metronome) and the heel contact was used as the accuracy of the gait event. Fifteen young group (YG, 24.7 ± 0.8 years) and 14 elderly (EG, 78.4 ± 5.5 years) people participated in the experiment. The temporal errors under two gait conditions (MET: walking with metronome; MET + BC: walking with metronome while counting backward) were compared for each group. The results revealed that all the temporal errors of EG were significantly greater than those of YG. While the addition of simultaneous cognitive tasks resulted in a significant increase in temporal error in both age groups, the coefficient of variation (CV) of the temporal error significantly increased only in the EG group. In other words, although heel contact accuracy with auditory stimuli was affected by the simultaneous cognitive task in both groups, it was demonstrated that the variability of the error in the young adults remained constant. Therefore, the time error measurement used in this study has the potential to be used as a tool to judge the gait instability of the elderly compared with young adults

A Pilot Study of the Efficiency of LSTM-Based Motion Classification Algorithms Using a Single Accelerometer

Inertial sensors are widely used for classifying the motions of daily activities. Although hierarchical classification algorithms were commonly used for defined motions, deep-learning models have been used recently to classify a greater diversity of motions. In addition, ongoing studies are actively investigating algorithm efficiency (e.g., training time and accuracy). Thus, a deep-learning model was constructed in this study for the classification of a given motion based on the raw data of inertial sensors. Furthermore, the number of epochs (150, 300, 500, 750, and 900) and hidden units (100, 150, and 200) were varied in the model to determine its efficiency based on training time and accuracy, and the optimum accuracy and training time was determined. Using a basic long short-term memory (LSTM), which is a neural network known to be suitable for sequential data, the data classification training was conducted on a common desktop PC with typical specifications. The results show that the accuracy was the highest (99.82%) with 150 hidden units and 300 epochs, while the training time was also relatively short (78.15 min). In addition, the model accuracy did not always increase even when the model complexity was increased (by increasing the number of epochs and hidden units) and the training time increased as a consequence. Hence, through suitable combinations of the two factors that constitute deep-learning models according to the data, the potential development and use of efficient models have been verified. From the perspective of training optimization, this study is significant in having determined the importance of the conditions for hidden units and epochs that are suitable for the given data and the adverse effects of overtraining