Fig 6 - uploaded by Julio Fajardo
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Finite State Machine demonstrating the opening/closing behavior of each finger on the prosthesis.

Finite State Machine demonstrating the opening/closing behavior of each finger on the prosthesis.

Source publication
Conference Paper
Full-text available
Numerous assistive devices possess complex ways to operate and interact with the subjects, influencing patients to shed them from their activities of daily living. With the purpose of presenting a better solution to mitigate issues generated by complex or expensive alternatives, a test comparing different user-prosthesis interfaces was elaborated t...

Contexts in source publication

Context 1
... the flexion/extension movements, except for the thumb, which possesses, additionally, a quadrature encoder using a PI position one for its rotation. This way, the prosthesis has the ability to perform different predefined gestures, i.e. pointing, power grip, etc. The functionality for each digit is illustrated in the Finite State Machine in Fig. ...
Context 2
... the flexion/extension movements, except for the thumb, which possesses, additionally, a quadrature encoder using a PI position one for its rotation. This way, the prosthesis has the ability to perform different predefined gestures, i.e. pointing, power grip, etc. The functionality for each digit is illustrated in the Finite State Machine in Fig. ...

Citations

... Eye-tracking measures included blink rate and pupillometry measures such as pupil diameter [19]- [21]. Among all the CW measures, NASA-TLX was the most frequently used method (28 out of 43 articles) [34], [35], [43]- [48], [53], [64]- [66], [69], [70], [72]- [82], [86]- [88]. The main reason for frequent use of NASA-TLX was determined as its capability to assess CW in motor tasks [58], [66], [92] and consideration of overall workload as well as the magnitude of each factor [49], [50]. ...
Full-text available
Article
Abstract—Limb amputation can cause severe functional disability for the performance of activities of daily living. Previous studies have found differences in cognitive demands imposed by prosthetic devices due to variations in their design. The objectives of this article were to 1) identify the range of cognitive workload (CW)assessment techniques used in prior studies comparing different prosthetic devices, 2) identify the device configurations or features that reduced CW of users, and 3) provide guidelines for designing future prosthetic devices to reduce CW. A literature search was conducted using Compendex, Inspec, Web of Science, Proquest, IEEE, Engineering Research Database, PubMed, Cochrane, andGoogle Scholar. Forty-three studies met the inclusion criteria. Findings suggested that CW of prosthetic devices was assesse dusing physiological, task performance, and subjective measures. However, due to the limitations of these methods, there is a need for more theoretical and model-based approaches to quantify CW. Device configurations such as hybrid input signals and use of multiodal feedback can reduce CW of prosthetic devices. Furthermore, to evaluate the effectiveness of a training strategy for reducingCW and improving device usability, both task performance and subjective measures should be considered. Based on the literature review, a set of guidelines was provided to improve the usability of future prosthetic devices and reduce CW.
... Vision-based prosthetic hands using deep learning technology have also been developed [30] [31] [32] [33]. The state-of-the-art approach offers new possibilities for the control of a dexterous prosthetic hand [34]. ...
Full-text available
Article
In this paper, we propose a novel control scheme for a vision-based prosthetic hand. To realize complex and flexible human-like hand movements, the proposed method fuses bimodal information. Combining information from surface EMG signals with object information from a vision sensor, the system can select an appropriate hand motion. The training/recognition using both sEMG signals and object images can be performed with a single deep neural network in an end-to-end manner. The bimodal sensor information enables the system to recognize the operator’s intended motion with higher accuracy than that of the conventional method using only sEMG signals. In addition, the generalization ability of the network is improved, so motion recognition robustness is enhanced against abnormal data that include partly noisy or missing samples. To verify the validity of the proposed approach, we prepared a dataset that contains the sEMG signals and the object images for 10 types of grasping motions. Three kinds of experiments were conducted: comparison of the proposed method with the conventional method, examination of the recognition robustness against partly noisy or missing samples, and challenges to recognize hand motions based on raw sEMG signals. The results revealed that the proposed bimodal network achieved considerably high recognition performance.