A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments

Chang, Yao‐Jen; Chen, Shu-Fang; Chuang, An-Fu

doi:10.1016/j.ridd.2011.08.010

Cited by 48 publications

(51 citation statements)

References 13 publications

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“…The more recent development of Smartphone and Tablet devices has allowed these functions to be carried out using easily accessible mainstream hardware and software. Other recently developed bespoke systems can guide people through a single task with several sub-steps such as food preparation (Kinempt; Chang, Chen & Chuang, 2011) or hand-washing (COACH; Mihailidis Carmichael & Boger, 2004) or remind people of events which they may have forgotten by playing back a series of photos taken during the day (SenseCam; Hodges et al, 2005).…”

Section: Assistive Technologymentioning

confidence: 99%

Technological memory aid use by people with acquired brain injury

Jamieson

Cullen

McGee‐Lennon

et al. 2015

Neuropsychological Rehabilitation

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Evans, investigated memory aid use by people with acquired brain injury (ABI) and found little use of technological memory aids. The present study aims to investigate use of technological memory aids and other memory aids and strategies ten years on, and investigate what predicts use. People with ABI and self-reported memory impairments (n = 81) completed a survey containing a memory aid checklist, demographic questions and memory questionnaires. Chi-square analysis showed that ten of 18 memory aids and memory strategies were used by significantly more people in the current sample than in . The most commonly used strategies were leaving things in noticeable places (86%) and mental retracing of steps (77%). The most commonly used memory aids were asking someone to remind you (78%), diaries (77%), lists (78%) and calendars (79%) and the most common technologies used were mobile phone reminders (38%) and alarms/ timers (38%). Younger people who used more technology prior to their injury and who use more non-technological memory aids currently were more likely to use technology. Younger people who used more memory aids and strategies prior to their injury and who rated their memory as poorer were more likely to use all types of memory aids and strategies.

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Section: Assistive Technologymentioning

confidence: 99%

Technological memory aid use by people with acquired brain injury

Jamieson

Cullen

McGee‐Lennon

et al. 2015

Neuropsychological Rehabilitation

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“…13 Recent years have seen the growth of wearable sensors such as wrist-worn accelerometers, wrist bands, and headgear for gesture recognition. More recently there is a surge of systems where sensors can be built into items of daily use such as clothing 5 for gesture recognition.…”

mentioning

confidence: 99%

Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment

Nelson

Waller

Robucci

et al. 2018

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction: This paper explores the feasibility of using touchless textile sensors as an input to environmental control for individuals with upper-extremity mobility impairments. These sensors are capacitive textile sensors embedded into clothing and act as proximity sensors. Methods: We present results from five individuals with spinal cord injury as they perform gestures that mimic an alphanumeric gesture set. The gestures are used for controlling appliances in a home setting. Our setup included a custom visualization that provides feedback to the individual on how the system is tracking the movement and the type of gesture being recognized. Our study included a two-stage session at a medical school with five subjects with upper extremity mobility impairment. Results: The experimenting sessions derived binary gesture classification accuracies greater than 90% on average. The sessions also revealed intricate details in participant's motions, from which we draw two key insights on the design of the wearable sensor system. Conclusion: First, we provide evidence that personalization is a critical ingredient to the success of wearable sensing in this population group. The sensor hardware, the gesture set, and the underlying gesture recognition algorithm must be personalized to the individual's need and injury level. Secondly, we show that explicit feedback to the user is useful when the user is being trained on the system. Moreover, being able to see the end goal of controlling appliances using the system is a key motivation to properly learn gestures.

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“…Section 4 uses different contexts and problems in home environments to present and validate the results of this care system. The conclusion describes the results of this study and future research directions [7,10,12].…”

Section: Research Purposesmentioning

confidence: 77%

“…In order to create a personalized and intellectualized care decision support system, this study combined fuzzy inference with ontology to create an intelligent context-aware care system. This study aims at the method for implementing context-aware service [4,[8][9][10].…”

Section: Introduction Research Background and Motivesmentioning

confidence: 99%

A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference

Chiang¹,

Liang²

2015

J Med Syst

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In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care.

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A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments

Cited by 48 publications

References 13 publications

Technological memory aid use by people with acquired brain injury

Technological memory aid use by people with acquired brain injury

Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment

A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference

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