Isu Shin

Biomechanical analysis of the circular friction hand massage

BACKGROUND A massage can be beneficial to relieve muscle tension on the neck and shoulder area. Various massage systems have been developed, but their motions are not uniform throughout different body parts nor specifically targeted to the neck and shoulder areas. OBJECTIVE Pressure pattern and finger movement trajectories of the circular friction hand massage on trapezius, levator scapulae, and deltoid muscles were determined to develop a massage system that can mimic the motion and the pressure of the circular friction massage. METHODS During the massage, finger movement trajectories were measured using a 3D motion capture system, and finger pressures were simultaneously obtained using a grip pressure sensor. RESULTS Results showed that each muscle had different finger movement trajectory and pressure pattern. The trapezius muscle experienced a higher pressure, longer massage time (duration of pressurization), and larger pressure-time integral than the other muscles. CONCLUSIONS These results could be useful to design a better massage system simulating human finger movements.

A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations

The short-time Fourier transform- (STFT-) based algorithm was suggested to distinguish falls from various activities of daily living (ADLs). Forty male subjects volunteered in the experiments including three types of falls and four types of ADLs. An inertia sensor unit attached to the middle of two anterior superior iliac spines was used to measure the 3-axis accelerations at 100 Hz. The measured accelerations were transformed to signal vector magnitude values to be analyzed using STFT. The powers of low frequency components were extracted, and the fall detection was defined as whether the normalized power was less than the threshold (50% of the normal power). Most power was observed at the frequency band lower than 5 Hz in all activities, but the dramatic changes in the power were found only in falls. The specificity of 1–3 Hz frequency components was the best (100%), but the sensitivity was much smaller compared with 4 Hz component. The 4 Hz component showed the best fall detection with 96.9% sensitivity and 97.1% specificity. We believe that the suggested algorithm based on STFT would be useful in the fall detection and the classification from ADLs as well.

Pre-impact fall detection using an inertial sensor unit

Falls are a major cause of injuries and deaths in older adults [1]. As for intervention strategies, one of the important problems in preventing or reducing the severity of injury in the elderly is to detect falls in its descending phase before the impact [2]. If a fall can be detected in its earliest stage in the descending phase, more efficient impact reduction systems can be implemented with a longer lead-time for minimizing injury [3,4]. In this study, we implemented a pre-impact fall detection algorithm using an inertial sensor unit. Totally, forty male volunteers participated in the experiment (three types of falls and seven types of ADLs). An inertia sensor unit, placed at waist, was used to measure subject’s acceleration, angular velocity and vertical angle during various activities. In order to detect pre-impact, the threshold of acceleration and angular velocity was set to 0.8g and 30°/s, respectively, based on the data from the first twenty subjects. Furthermore, the threshold of vertical angle was set to 30° because the maximum angle in the ADL did not exceed 30°. This fall detection algorithm was evaluated for another twenty subjects. The results showed that both acceleration and angular velocity during three different falls were greater than the threshold during several ADLs and the vertical angle did not exceed 30°. The vertical angle exceeded 30° only during sit-lying activity, but the acceleration did not reach 0.8g (Table ​(Table1).1). Based on the pre-impact fall detection algorithm, no false detection was found (100% sensitivity) for all falls. Table 1 Peak acceleration, angular velocity and vertical angle during falls and ADLs. Furthermore, no incorrect detection was found (100% specificity) for all ADLs. The lead time was 474 ± 38.3ms, 590.3 ± 122.6ms and 527 ± 62.3ms in the backward, the forward and the side falls, respectively. In this study, a pre-impact fall detection algorithm was developed using an inertial sensor unit. The present pre-impact fall detection algorithm can be implemented with a wearable fall injury minimization system to track a user’s body movement.

Kinematic Analysis of Lower Extremity and Evaluation of Skill of Skier Using Parameters of Inertial Sensors During Ski Simulator Exercise

Abstract: In this study, joint angles of the lower extremity and inertial sensor data such as accelerations and angularvelocities were measured during a ski simulator exercise in order to evaluate the skill of skiers. Twenty experts andtwenty unskilled skiers were recruited for the study. All expert skiers held the certificates issued by the Korea SkiInstructors Association. A three-dimensional motion capture system and two inertial sensors were used to acquirejoint movements, heel acceleration and heel angular velocity during ski simulator exercises. Pattern variation valueswere calculated to assess the variations in ski simulator motion of expert and unskilled skiers. Integral ratio of rollangular velocity was calculated to determine the parallel alignment of the two feet. Results showed that ski expertsshowed greater range of motion of joint angle, peak to peak amplitude(PPA) of heel acceleration and PPA of heelangular velocity than unskilled skiers. Ski experts showed smaller pattern variations than unskilled skiers. In addi-tion, the integral ratio of roll angular velocity in ski experts was closer to 1. Inertial sensor data measurements duringthe ski simulator exercises could be useful to evaluate the skill of the skier.Key words: ski simulator, inertial sensor, kinematics

Kinematic Study of Lower Extremity Movements in Unskilled and Expert Snowboarders During Snowboard Simulator Exercises

Abstract: In this study, joint angles of the lower extremity and tibial acceleration and angular velocity were measuredduring a snowboard simulator exercises in order to evaluate the skill of snowboarders. Ten unskilled and ten expertsnowboarders were recruited for the study. A three-dimensional motion capture system and two inertial sensor mod-ules were used to acquire joint movements, acceleration and angular velocity of the lower extremities during snow-board simulator exercises. Pattern variations were calculated to assess variations in the snowboard simulator motionof unskilled and expert snowboarders. Results showed that expert snowboarders showed greater range of motionin joint angles and greater peak to peak amplitude in acceleration and angular velocity for tibia than unskilled snow-boarders. The unskilled snowboarders did not show symmetrical shape(same magnitude but opposite direction) intibial angular velocity during two edge turns in snowboard simulator exercises. The expert snowboarders showedsmaller pattern variations for joint angle of lower extremity, tibial acceleration and tibial angular velocity thanunskilled snowboarders. Inertial sensor data and pattern variations during the snowboard simulator exercises couldbe useful to evaluate the skill of snowboarders.Key words: snowboard simulator, inertial sensor, kinematics, pattern variation