Authors:Richard Sather III, Mahsa Soufineyestani, Nabiha Imtiaz, Arshia Khan First page: 1 Abstract: As per the World Population prospects (19th revision), in 2019 every 11th person (11% of the population) was aged 65 or older and by 2050 every 6th person (16% of the world population) will be aged 65 or older. This rapid growth in people aged 65 and above has and will continue to pose some health management concerns, especially in the elderly with chronic ailments. The need for care provision for the elderly has provoked an exploration of various solutions to address elderly care management. Non-pharmacological interventions that utilize technology, such as robotics, are solutions that have proven to prolong independence and delay the admission of elderly into assisted care facilities. This paper will explore the various types of robotic solutions that are currently available to offer elderly care. This study will look at robotic solutions that are humanoid, animal-like, and robots that do not resemble humans or animals and their applications in elderly care. The various applications of robotics and the respective types of robots utilized in the provision of care in elderly care will be discussed as well. PubDate: 2021-05-19 DOI: 10.5430/ijrc.v3n1p1 Issue No:Vol. 3, No. 1 (2021)
Authors:Richard Scott Byfield, Richard Weng, Morgan Miller, Yunchao Xie, Jheng-Wun Su, Jian Lin First page: 13 Abstract: In recent years, advances in human robot interaction (HRI) has shown massive potential for universal control of robots. Among them, electromyography (EMG) signals generated by motions of muscles have been identified as an important and useful source. Powered by recently emerged machine learning algorithms, real-time classification has been proved applicable to control robots. However, collecting EMG signals with minimum number of electrodes for real-time classification and robotic control is still a challenge. In this paper, we demonstrate that twenty five robotic commands in a robotic arm can be controlled in real time by using the EMG signals collected from only two pairs of active surface electrodes on each forearm of human subjects. To achieve this task, a variety of tested ML models for this classification were tested. Among them, the Gaussian Naïve Bayes (GNB) achieved an accuracy of >96%. This unprecedented level of classification accuracy of the EMG signals collected from the least number of active electrodes suggest that by combination of optimized electrode configuration and a suitable ML model, the capability of robotic control can be maximized. PubDate: 2021-08-02 DOI: 10.5430/ijrc.v3n1p13 Issue No:Vol. 3, No. 1 (2021)
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