The purpose of this study was to determine how smartphone use posture affects biomechanical variables and muscle activities. Eleven university students(age: 22.2±2.6 yrs, height: 176.6±4.7 cm, weight: 682.2±73.8 N) who have no musculoskeletal disorder were recruited as the subjects according to havi...
The purpose of this study was to determine how smartphone use posture affects biomechanical variables and muscle activities. Eleven university students(age: 22.2±2.6 yrs, height: 176.6±4.7 cm, weight: 682.2±73.8 N) who have no musculoskeletal disorder were recruited as the subjects according to having experience in using the smartphone for more than one year. Kinematic data were acquired by using two digital camcoders (Motion Master100, Visol Inc, 200 fields/s). Muscular load in three forearm/hand muscles in the right arm and right trapezius muscles were measured using electromyograph(QEMG8, Laxtha Inc. korea, sampling frequency = 1024 Hz, gain = 1,000, input impedance > 10 Ω, CMRR > 100 dB). Thumb finger force were registered using a finger pressure measurement equipment(Wireless Finger TPS system, Pressure Profile Systems, Inc, 40 Hz). In order to conduct 3D motion analysis, four reflective markers were attached to finger joints and this research used Kwon3D program version 3.10. Finger task movements are divided into ten critical events and five phases through camera-used analyses. Direct Linear Transformation(DLT) was used to gain three-dimensional coordinate values of each control point and joint points equipped with the reflective markers. Rotation angle, inclination angle, angular velocity, muscle activity, and thumb finger force were determined for each trial. For each dependent variable, a one-way analysis of variance (ANOVA) with repeated measures was performed to test if significant difference existed among different three conditions (p<.05). The result showed that angular velocity of the first metacarpal were increased in DESK posture compared with STAND posture during SU phase. These indicated that ROM(Range of motion) of the thumb segment in DESK posture was greater than those of STAND posture. The average nEMG values of FDI(First dorsal interosseous) were less in SIT posture compared with STAND posture during SR phase. These indicated that smartphone postures may effect the thumb ROM and muscle activity in upper extremity. In average and maximum finger force of the thumb, there were no significant difference among the smartphone use postures. It seems that difference of smart phone use posture didn't affect on the thumb finger force directly. This has led to suggestions of the need for further kinetic and EMG analyses to evaluate best assess and characterize with smartphone use.
The purpose of this study was to determine how smartphone use posture affects biomechanical variables and muscle activities. Eleven university students(age: 22.2±2.6 yrs, height: 176.6±4.7 cm, weight: 682.2±73.8 N) who have no musculoskeletal disorder were recruited as the subjects according to having experience in using the smartphone for more than one year. Kinematic data were acquired by using two digital camcoders (Motion Master100, Visol Inc, 200 fields/s). Muscular load in three forearm/hand muscles in the right arm and right trapezius muscles were measured using electromyograph(QEMG8, Laxtha Inc. korea, sampling frequency = 1024 Hz, gain = 1,000, input impedance > 10 Ω, CMRR > 100 dB). Thumb finger force were registered using a finger pressure measurement equipment(Wireless Finger TPS system, Pressure Profile Systems, Inc, 40 Hz). In order to conduct 3D motion analysis, four reflective markers were attached to finger joints and this research used Kwon3D program version 3.10. Finger task movements are divided into ten critical events and five phases through camera-used analyses. Direct Linear Transformation(DLT) was used to gain three-dimensional coordinate values of each control point and joint points equipped with the reflective markers. Rotation angle, inclination angle, angular velocity, muscle activity, and thumb finger force were determined for each trial. For each dependent variable, a one-way analysis of variance (ANOVA) with repeated measures was performed to test if significant difference existed among different three conditions (p<.05). The result showed that angular velocity of the first metacarpal were increased in DESK posture compared with STAND posture during SU phase. These indicated that ROM(Range of motion) of the thumb segment in DESK posture was greater than those of STAND posture. The average nEMG values of FDI(First dorsal interosseous) were less in SIT posture compared with STAND posture during SR phase. These indicated that smartphone postures may effect the thumb ROM and muscle activity in upper extremity. In average and maximum finger force of the thumb, there were no significant difference among the smartphone use postures. It seems that difference of smart phone use posture didn't affect on the thumb finger force directly. This has led to suggestions of the need for further kinetic and EMG analyses to evaluate best assess and characterize with smartphone use.
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