Motion Analysis of Transfer Operation from Bed to Wheelchair for Care-giver and -receiver with Wearable Motion Capture

Author(s):  
Takehito Kikuchi ◽  
Okito Shimazu ◽  
Yasuhiro Yamamoto ◽  
Mikiko Nakano ◽  
Sachiko Ichiyama ◽  
...  
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  

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.


2006 ◽  
Vol 21 (1) ◽  
pp. 10-16
Author(s):  
Brenda Wristen ◽  
Sharon Evans ◽  
Nicholas Stergiou

This study was intended to examine whether differences exist in the motions employed by pianists when they are sight-reading versus performing repertoire and to determine whether these differences can be quantified using high-speed motion capture technology. A secondary question of interest was whether or not an improvement in the efficiency of motion could be observed between two sight-reading trials of the same musical excerpt. This case study employed one subject and a six-camera digital infrared camera system to capture the motion of the pianist playing two trials of a repertoire piece and two trials of a sight-reading excerpt. Angular displacements and velocities were calculated for bilateral shoulder, elbow, wrist, and index finger joints. The findings demonstrate the usefulness of high-speed motion capture technology for analyzing motions of pianists during performance, showing that the subject's motions were less efficient in sight-reading tasks than is repertoire tasks.


Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1076
Author(s):  
Laisi Cai ◽  
Dongwei Liu ◽  
Ye Ma

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.


2004 ◽  
Vol 16 (Supplement) ◽  
pp. 39-40
Author(s):  
Takayuki Shima ◽  
Takaya Terada ◽  
Yoshinobu Izumi ◽  
Shinichi Takeda ◽  
Kimiko Ema ◽  
...  
Keyword(s):  

10.2196/26825 ◽  
2020 ◽  
Author(s):  
Hanna Marie Röhling ◽  
Patrik Althoff ◽  
Radina Arsenova ◽  
Daniel Drebinger ◽  
Norman Gigengack ◽  
...  

Measurement ◽  
2020 ◽  
Vol 149 ◽  
pp. 107024 ◽  
Author(s):  
Ryan Sers ◽  
Steph Forrester ◽  
Esther Moss ◽  
Stephen Ward ◽  
Jianjia Ma ◽  
...  

2019 ◽  
Vol 35 (1) ◽  
pp. 80-86 ◽  
Author(s):  
Gustavo Ramos Dalla Bernardina ◽  
Tony Monnet ◽  
Heber Teixeira Pinto ◽  
Ricardo Machado Leite de Barros ◽  
Pietro Cerveri ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document