Development of hands-free wheelchair device based on head movement and bio-signal for quadriplegic patients

Lee, Geonhee; Kim, Keunhyun; Kim, Jaehyo

doi:10.1007/s12541-016-0045-5

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…For instance, EMG signals from hand muscles have been used to control wheelchair movements [10]. Additionally, EMG signals generated around the temples during tooth clenching have been employed to control the start and stop functions of the wheelchair [11]. These EMG-based control methods offer flexibility in wheelchair control.…”

Section: Introductionmentioning

confidence: 99%

EEG and eye-EMG Combined Control of Rehabilitation Wheelchair Using an Improved Genetic Algorithm

sun,

yang,

Shan

et al. 2024

Preprint

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In this paper, we present a combined control system for wheelchairs based on bioelectricity sensors, aimed at enhancing the mobility range of individuals with rehabilitation patients. The approach leverages the capabilities of bioelectricity sensors to read both the action bioelectricity signals of the accessory eye organs (AEO) and mental power level, i.e. an EEG signal and an eye-EMG signal, providing an innovative solution for enhancing the control mechanism of wheelchairs, thereby improving mobility and independence for individuals with movement disorders. The system achieves this by comparing the sample library established ahead, processing the action bioelectricity signals of AEO, and converting them into combined control instructions for the wheelchair. By integrating it with the mental power level and obtaining multiple control instructions, the system's stability is significantly improved. This system enables the wheelchair to perform various movements such as left-turning, right-turning, forward moving, stopping, accelerating, and decelerating. Additionally, the control stability of wheelchair movements is enhanced. To optimize the sample library of AEO action signals, we employ a genetic algorithm that utilizes roulette selection with random acceptance to increase convergence speed. The individual fitness of the population is improved through parent crossover and sorting differential mutation operators. By optimizing the sample library based on bioelectricity sensors, the action signals are classified using eigenvalues, resulting in a further improvement in classification accuracy. The performance of the combined control system is evaluated by utilizing metrics such as accuracy rate, false activation rate, and misjudgment rate. The experimental results validate the excellent performance of the system. The wheelchair was successfully controlled to move towards the destination along a predetermined path using the combined control system. Overall, the combined control system expands the range of activities for rehabilitation patients.

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Section: Introductionmentioning

confidence: 99%

EEG and eye-EMG Combined Control of Rehabilitation Wheelchair Using an Improved Genetic Algorithm

sun,

yang,

Shan

et al. 2024

Preprint

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“…As described in [5] and [6], head movement sensorbased detection can be operated either with an inertial technique or a bio-signal technique. The former senses acceleration and rotational forces to measure pitches, rolls, and yaws of an object.…”

Section: Introductionmentioning

confidence: 99%

Visual and Gyroscope Sensor for Head Movement Controller System on Meal-Assistance Application

Yunardi

Dina

Agustin

et al. 2020

mjee

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Head movement utilizes gestures to aid people with disabilities so that they can have hands-free human-computer interaction. Currently, motion-based sensor is the most widely used approach to recognize head gestures. Identification of head movement is important to control a robotic manipulator in an assisting device. However, the most effective methodologies to assess head angular movements are yet to be discovered. This paper combines two algorithms, the visual sensor and the gyro sensor, to identify head orientation movement with high precision. Head orientations were measured using data distribution and this was done with a meal-assistance robot manipulator used in a sitting position. Evaluation of the accuracy of the system shows a visual sensor and gyro sensor. Experimental results show that a correct head movement with the average accuracy is 82%. Therefore, we propose the application of position control of meal assistive robot based on user's head movement in a sitting position.

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“…Experienced users also report having difficulties performing basic tasks that involve controlling the joystick and maneuvering [13]. Researchers have contributed with many instruments aimed at helping people to drive wheelchairs [14]- [24]. They are mainly either joystick variations or alternative driving systems.…”

Section: Introductionmentioning

confidence: 99%

Tactile Sensor-Based Steering as a Substitute of the Attendant Joystick in Powered Wheelchairs

Trujillo-León

Bachta

Vidal‐Verdú

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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Attendant joysticks of powered wheelchairs are devices oriented to help caregivers. Diseases and disabilities such as dementia, spinal cord injuries or blindness make the user unable to drive the chair by his or her own. However, this device is not intuitive to use, especially for old people. Proper processing of the information provided by two tactile sensors in the handlebar achieves control signals that allow an easy and intuitive driving. This is done in this paper, where the performance of this approach is evaluated in comparison with that of the joystick by means of objective measurements as well as questionnaires to obtain the subjective perception of the participants in the experiments. The results show a better performance of the handlebar in terms of error in following a trajectory, collisions with the surrounding furniture, and user feeling related to ease of use, comfort, required training, usefulness, safety, and fatigue.

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Development of hands-free wheelchair device based on head movement and bio-signal for quadriplegic patients

Cited by 6 publications

References 3 publications

EEG and eye-EMG Combined Control of Rehabilitation Wheelchair Using an Improved Genetic Algorithm

EEG and eye-EMG Combined Control of Rehabilitation Wheelchair Using an Improved Genetic Algorithm

Visual and Gyroscope Sensor for Head Movement Controller System on Meal-Assistance Application

Tactile Sensor-Based Steering as a Substitute of the Attendant Joystick in Powered Wheelchairs

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