Human functional pattern recognition with focus on hand detection in egocentric vision through an artificial  intelligence approach

Eyitomilayo Yemisi Babatope; Mario López-Rodríguez; Jesus Alejandro Acosta-Franco; Mireya Saraí García-Vázquez; Alejandro Álvaro Ramírez-Acosta

doi:10.1117/12.2632894

3 October 2022 Human functional pattern recognition with focus on hand detection in egocentric vision through an artificial intelligence approach

Eyitomilayo Yemisi Babatope, Mario López-Rodríguez, Jesus Alejandro Acosta-Franco, Mireya Saraí García-Vázquez, Alejandro Álvaro Ramírez-Acosta

Author Affiliations +

Proceedings Volume 12225, Optics and Photonics for Information Processing XVI; 1222504 (2022) https://doi.org/10.1117/12.2632894
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Good health and functional ability are important for individuals to lead fulfilling mental, psychological, and social lives. The diseases such as Dementia causes irreversible damage, decline in cognition, function, and behavior which translates into difficulty in independently performing daily tasks. Studies showed that assessment of Instrumental activities of daily living(IADLs) correlate with humans' cognitive and functional status. Analysis of biomechanical markers such as hand movement/use was done with artificial intelligence(AI). We present an optimized AI algorithm for hand detection in the analysis of egocentric video recordings. This improved AI algorithm is based on a probabilistic approach where hand regions are detected in egocentric videos. They then feed the human functional pattern recognition process. To evaluate the performance of our proposal we use a dataset containing the four functional patterns organized into four classes, based on the prehensile patterns of the hands: strength-precision, and on the kinematics of the instruments: displacementhandling. This work was inspired by a previous work done by our group, where biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern. The result of our proposal yielded an accuracy of 87.5% in recognizing strength-precision and displacement-handling movement patterns when evaluating the test database with information from Segmented and Not-Segmented videos. This resulted in a single video that changed its classification ratio between the two subsets. This can be of great potential in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.

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Eyitomilayo Yemisi Babatope, Mario López-Rodríguez, Jesus Alejandro Acosta-Franco, Mireya Saraí García-Vázquez, and Alejandro Álvaro Ramírez-Acosta "Human functional pattern recognition with focus on hand detection in egocentric vision through an artificial intelligence approach", Proc. SPIE 12225, Optics and Photonics for Information Processing XVI, 1222504 (3 October 2022); https://doi.org/10.1117/12.2632894

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