• ETRI Journal
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• v.33 no.4
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• pp.569-579
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• 2011

This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time-series activity images acquired with a single stereo camera by co-registering a 3D body model to the stereo information. The estimated joint-angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint-angle-based HAR has been compared to that of a conventional binary and depth silhouette-based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_1
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• pp.693-698
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• 2021

As technology advances, there is increasing need for research in different fields where this technology is applied. On of the most researched topic in computer vision is Human activity recognition (HAR), which has widely been implemented in various fields which include healthcare, video surveillance and education. We therefore present in this paper a human activity recognition system based on scale and rotation while employing the Kinect depth sensors to obtain the human skeleton joints. In contrast to previous approaches that use joint angles, in this paper we propose that each limb has an angle with the X, Y, Z axes which we employ as feature vectors. The use of the joint angles makes our system scale invariant. We further calculate the body relative direction in the egocentric coordinates in order to provide the rotation invariance. For the system parameters, we employ 8 limbs with their corresponding angles each having the X, Y, Z axes from the coordinate system as feature vectors. The extracted features are finally trained and tested with the Long short term memory (LSTM) Network which gives us an average accuracy of 98.3%.

• Journal of the Korean Orthopaedic Association
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• v.54 no.4
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• pp.343-352
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• 2019

Purpose: The purpose of this study was to document the sonographic morphology of the subscapularis footprint, particularly the 1st facet, of the non-pathologic subscapularis tendon and footprint, and analyze the correlation between the size of the 1st facet and the demographic variables. Materials and Methods: Between March 2015 and December 2017, retrospectively data analysis was performed for the ultrasound (US) scans of 115 consecutive shoulder (mean age 53.4 years, range 23-74 years) with non-pathologic subscapularis tendon and footprint. The sonographic findings of the 1st facet of the subscapularis footprint was a very unique, flat, broad, and plane angle in the upward direction, which were distinguished from the other facets. On US, the transverse (medio-lateral) and longitudinal (superior-inferior) length of the 1st facet on axis of the humerus shaft were recorded. The demographic variables, including age, site, body height, weight, body mass index (BMI), and arm length, were reviewed. Results: On US, the mean transverse length of the 1st facet was 12.75 mm (range 10.54-14.50 mm, standard deviation [SD] 0.712) and the mean longitudinal length was 12.22 mm (range 9.20-13.30 mm, SD 0.888). The transverse and longitudinal length of the size of the 1st facet were significantly greater in males than in females (p<0.001, p=0.001). Of the demographic data (body height, weight, BMI, arm length) that showed a significant positive linear correlation, the correlation with body height (transverse r=0.749, p<0.001; longitudinal r=0.642, p<0.001) showed the strongest relationship, and the correlation with the BMI was weakly related. The relationships between the size of the 1st facet to site/age were not statistically significant or appeared to have no linear correlation. Conclusion: The structural and morphologic features of the 1st facet of the subscapularis footprint on the US were identified. This will provide anatomic knowledge of an US examination for subscapularis tendon pathology.