Design and Assessment of a Single-size Semi-soft Assistive Mitten for People with Cervical Spinal Cord Injuries

Kaneishi, Daisuke; Leu, Jessica; O'Donnell, Julia; Affleck, Campbell; Matthew, Robert Peter; McPherson, Andrew; Tomizuka, Masayoshi; Stuart, Hannah

doi:10.1109/humanoids43949.2019.9035027

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Seven studies used bending sensors to detect finger (Ab Patar et al, 2014 ; Popov et al, 2017 ; Xiloyannis et al, 2018 ; Park et al, 2019 ) or wrist motion (Kaneishi et al, 2019 ; Rose and O'Malley, 2019 ) to control hand orthoses, or wrist motion to control a shoulder-elbow-orthosis (Koo et al, 2009 ). Four studies detected residual upper limb movement by using inertial measurement units (IMUs) attached to a segment of the upper limb.…”

Section: Reviewmentioning

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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