Poster 93: Characterization of Upper Extremity Motion in Grasping and Reaching using Inertial Sensor Based Motion Capture System

PM&R ◽  
2017 ◽  
Vol 9 ◽  
pp. S165-S166
Author(s):  
Hyung Seok Nam ◽  
Han Gil Seo ◽  
Sungwan Kim
Keyword(s):  
Upper Extremity ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Motion Capture System ◽  
Extremity Motion

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

Spatial Kinematic Analysis of the Upper Extremity Using a Biplanar Videotaping Method

1981 ◽  
Vol 103 (1) ◽  
pp. 11-17 ◽  
Author(s):  
N. A. Langrana
Keyword(s):  
Upper Extremity ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Assistive Device ◽  
Kinematic Data ◽  
Computer Aided ◽  
Extremity Motion ◽  
Accuracy And Repeatability ◽  
Three Dimensional Space

A biplanar videotaping system is used to generate spatial kinematic data of an upper extremity motion. The technique is based upon the characterization of each segment by four points in three-dimensional space using biplanar videotaping and subsequent analysis by computer-aided descriptive geometry. The tests were conducted to determine the system’s accuracy and repeatability. The results of the joint kinematics of the test subjects performing a diagonal reaching activity with and without an orthosis (or an assistive device) are presented.

scholarly journals Foot Position Measurement during Assistive Motion for Sit-to-Stand Using a Single Inertial Sensor and Shoe-Type Force Sensors

2021 ◽  
Vol 18 (19) ◽  
pp. 10481
Author(s):  
Kodai Kitagawa ◽  
Ibai Gorordo Fernandez ◽  
Takayuki Nagasaki ◽  
Sota Nakano ◽  
Mitsumasa Hida ◽  
...  
Keyword(s):  
Motion Capture ◽  
Inertial Sensor ◽  
Body Height ◽  
Force Sensors ◽  
Motion Capture System ◽  
Position Measurement ◽  
Sit To Stand ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Foot Position

Assistive motion for sit-to-stand causes lower back pain (LBP) among caregivers. Considering previous studies that showed that foot position adjustment could reduce lumbar load during assistive motion for sit-to-stand, quantitative monitoring of and instructions on foot position could contribute toward reducing LBP among caregivers. The present study proposes and evaluates a new method for the quantitative measurement of foot position during assistive motion for sit-to-stand using a few wearable sensors that are not limited to the measurement area. The proposed method measures quantitative foot position (anteroposterior and mediolateral distance between both feet) through a machine learning technique using features obtained from only a single inertial sensor on the trunk and shoe-type force sensors. During the experiment, the accuracy of the proposed method was investigated by comparing the obtained values with those from an optical motion capture system. The results showed that the proposed method produced only minor errors (less than 6.5% of body height) when measuring foot position during assistive motion for sit-to-stand. Furthermore, Bland–Altman plots suggested no fixed errors between the proposed method and the optical motion capture system. These results suggest that the proposed method could be utilized for measuring foot position during assistive motion for sit-to-stand.

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