Head Movement Control of Powered Wheelchair

Șolea, Răzvan; Margarit, A. S.; Cernega, Daniela; Şerbencu, Adrian

doi:10.1109/icstcc.2019.8885844

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…It was already used by Antonelli et al ( 2009 ) to control lower limb orthoses, allows wearability, and has similarities in signal acquisition and processing to EMG and FMG. IDS based on head movement are primarily used for powered wheelchair control (Kupetz et al, 2010 ; Solea et al, 2019 ). However, this IDS is primarily used for people for whom head movement is one of the sole possible movements.…”

Section: Discussionmentioning

confidence: 99%

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

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Section: Discussionmentioning

confidence: 99%

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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“…In this case, a Raspberry Pi 4 Model B 4GB RAM was used (figure 2). This tiny desktop computer with dual-display can be a network AI core, a robot brain, factory controller, a smart home hub, and much more (figure 2) [10][11][12]. The role of a SBC is to support the IoT ecosystem.…”

Section: Methodsmentioning

confidence: 99%

Integrating LMS and IoT in the Educational System

Ganea

Craciun

Aramă

et al. 2020

Ann_UGAL_Math_Phys_Mec

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Context: Nowadays, developing computer skills is important for a student's success. Objective: Smart classrooms bring new experiences for both students and teachers not only in this current context, by increasing safety, data availability, teaching and learning optimized processes, and much more. Method: For improving the transmit and receive process (TX and RX) performances of the educational experience, we propose a system based on a Learning Management System (LMS) and the Internet of Things (IoT). Results: The results of this concept can be perceived at a theoretical level for now. LMS and IoT offer high tech solutions for sustainable and smart classrooms. The immediate result is the improvement of everyday activities and learning methods. Conclusion: The aim of this paper is to describe the efficiency of the LMS and IoT infrastructure in restructuring the traditional learning methods.

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“…In Rudigkeit and Gebhard (2019), an adaptive head motion system allows the user to control both an assistive robotic arm as well as a visual interface in a computer. Another head gesture recognition method is implemented by Solea et al (2019) using a head pose control algorithm to operate a power wheelchair. The control system translates the head motions into speed and directional device control.…”

Section: Related Workmentioning

confidence: 99%

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

et al. 2022

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Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.

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Head Movement Control of Powered Wheelchair

Cited by 13 publications

References 11 publications

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Integrating LMS and IoT in the Educational System

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

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