Women’s Healthcare
September 1, 2021
France
October 1, 2021Article of the Month – September 2021
Hand Gestures for Elderly Care Using a Microsoft Kinect
Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
School of Engineering, University of South Australia, Mawson Lakes SA 5095, Australia.
Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne VIC 3207, Australia.
Munir Oudah, Ali Al-Naji, and Javaan Chah
Importance
The link between humans and computers, called human-computer interaction (HCI) techniques, has the potential to improve quality of life, where analysis of the information collected from humans through computers allows personal patient requirements to be achieved. Among them, a computer vision system for helping elderly patients currently attracts a large amount of research interest to avail of personal requirements.
Objectives
This paper proposes a real-time computer vision system to recognize hand gestures for elderly patients who are disabled or unable to translate their orders or feelings into words.
Design and Settings
The paper was conducted using four elderly participants (3 male and 1 female) between the age of 60 and 75 years old and one adult (33 years old). This study adhered to the Declaration of Helsinki ethical principles (Finland 1964) where a written informed consent form was obtained from all participants after a full explanation of the experimental procedures. The experiment was for approximately 1-h for each participant at home environment and repeated at various times to obtain sufficient outcomes.
Outcomes and Measures
The proposed system uses a Microsoft Kinect v2 Sensor, installed in front of the elderly patient, to recognize hand signs that correspond to a specific request and sends their meanings to the care provider or family member through a microcontroller and global system for mobile communications (GSM)
Results
The system presented 5 hand gestures (1, 2, 3, 4 and 5) using a finger counting technique. the total number of tests per unit gesture was 44 for all participants and 264 tested samples for overall tests. The result presented for false recognition was because detection was more sensitive for gesture number “3” than the other gestures.
Conclusions
In this paper, a real-time computer vision system to recognize hand gestures for elderly patients was proposed using Microsoft Kinect sensor v2, Arduino Nano Microcontroller and GSM. The 5 hand signs recognized included the open palm gestures, 1, 2, 3 and 4 sign fingers which identified the elderly patients’ requests such as water, meal, toilet, help and medicine, sent as a message through the GSM Module Sim800l controlled by an Arduino Nano Microcontroller. Although the proposed system was accurate, cost-effective, easy to use and portable, further studies on robustness, low light condition and detection range are needed to achieve better outcomes.
Relevance to Healthcare Field
America has an increasingly aging population with over 46 million people that are over 65 years old. This growth brings more opportunities for new artificial intelligence (AI) health care methods to be developed and used. Communication becomes difficult for some when reaching a certain age, and sometimes individual disabilities do not allow adequate interpersonal communication. This paper proposes Human-Computer Interaction (HCI) based on a computer vision system using hand gestures. Holding up one to five fingers relates to personal requests such as water, meal, toilet, help, and medication. These gestures are captured by the Microsoft Kinect v2 Sensor and transmitted to the Arduino Nano microcontroller, which translates the gesture into a message sent wirelessly to the care provider through a global system for mobile communication (GSM) modem. The study found more recognizable than unrecognizable gestures from participants, resulting in an efficient, reliable, safe, comfortable, and cost-effective method with a faster response from the caregiver. Once again, AI has proven to be a novel way to solve a unique problem.