Benefits of the Cybathlon 2020 experience for a prosthetic hand user: a case study on the Hannes system

Abstract

Background Cybathlon championship aims at promoting the development of prosthetic and assistive devices capable to meet users’ needs. This paper describes and analyses possible exploitation outcomes of our team’s (REHAB TECH) experience into the Powered Arm Prosthesis Race of the Cybathlon 2020 Global Edition, with the novel prosthetic system Hannes. In detail, we present our analysis on a concurrent evaluation conducted to verify if the Cybathlon training and competition positively influenced pilot’s performance and human-technology integration with Hannes, with respect to a non-runner Hannes user. Methods Two transradial amputees were recruited as pilots (Pilot 1 and Pilot 2) for the Cybathlon competition and were given the polyarticulated myoelectric prosthetic hand Hannes. Due to COVID-19 emergency, only Pilot 1 was trained for the race. However, both pilots kept Hannes for Home Use for seven weeks. Before this period, they both participated to the evaluation of functionality, embodiment, and user experience (UX) related to Hannes, which they repeated at the end of the Home Use and right after the competition. We analysed Pilot 1’s training and race outcomes, as well as changes in the concurrent evaluation, and compared these results with Pilot 2’s ones. Results The Cybathlon training gradually improved Pilot 1’s performances, leading to the sixth place with a single error in task 5. In the parallel evaluation, both pilots had an overall improvement over time, whereas Pilot 2 experienced a deterioration of embodiment. In detail, Pilot 1, who followed the training and raced the Cybathlon, improved in greater way. Conclusion Hannes demonstrated to be a valuable competitor and to perform grasps with human-like behaviors. The higher improvements of Pilot 1, who actively participated in the Cybathlon, in terms of functionality, embodiment and UX, may depend on his training and engagement in the effort of achieving a successful user-prosthesis interaction during the competition. Tasks based on Cybathlon’s ones could improve the training phase of a prosthetic user, stimulating dexterity, prosthetic integration, and user perception towards the prosthesis. Likewise, timed races or competitions could facilitate and accelerate the learning phase, improving the efficiency and efficacy of the process.

How the CYBATHLON Competition Has Advanced Assistive Technologies

“…This is also the case for CYBATHLON, where the pilot contributes significantly to a team's performance. But-more importantly, from a daily-life perspective-the intensive training and competitive motivation ahead of the event led to increased home use in some cases (61,138). This finding was further supported by individual narrations from pilots who reported that their perception of and confidence in their device have improved as a result of their preparation, even after being long-term users of assistive technology.…”

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

“…The F1Score of the classifier during the 2FB condition tested with able-bodied subjects ( Figure 5A ) was very high (94.93% ± 3.94), demonstrating that the only sensors available on Hannes (motor-side current and encoder position) provide sufficient information for an object stiffness discrimination task using an NLR algorithm. However, it is necessary to consider that the classifier, during this experiment, was only tested on six objects of different shapes but with almost the same dimensions (chosen to replicate the objects used during the Cybathlon race, Caserta et al, 2022 ). Furthermore, the F1Score obtained by able-bodied subjects ( Figure 5A ) on discriminating the object stiffness was also very good, proving the usability and efficacy of this feedback approach on a user case.…”

“…The experimental setup that used for performing the entire experiment ( Figure 1 ) was composed of (A) the myoelectric prosthesis Hannes, fixed on a rigid cone; (B) a custom master-board to control the hand, decode the stiffness of the grasped object, and communicate with the PC via Bluetooth; (C) two EMG sensors (standard Ottobock, 13E200 = 50 AC) to close or open the hand; (D) an eccentric rotating mass (ERM) vibromotor to convey the feedback; (E) a power supply for the prosthetic system; (F) two wristbands to attach the EMG sensors and the vibromotor to the subject's forearm; (G) three rigid objects and three soft objects with spherical, cubic, and cylindrical shape used during the Cybathlon 2020 edition (Medynski and Rattray, 2011 ; Caserta et al, 2022 ); (H) a laptop to choose the feedback condition and to collect the data; (I) a keyboard, placed in front of the subject, to press the left (rigid object) and right (soft object) arrows to indicate the guessed stiffness of the grasped object; and (J) headphones reproducing white noise to prevent the users from hearing the prosthesis motor.…”

“…For instance, when the user is taking an object from a bag without looking at it or when the light in the environment is off. This situation was also treated by the Cybathlon 2020 competition, which introduced the Haptic Box task, considering it as a common ADL (Caserta et al, 2022 ).…”