Applying an Innovative User-Centric Co-Creation (UC3) Approach in Developing Intelligent Wearable Robots for Elderly Assistance: From a Transdisciplinary Lens

Abstract

Mobility difficulties is a major public health issue which affects the ability of older adults to perform daily living activities and its prevalence increased with age. Assistive technology such as wearable robots has the potential to support older adults with mobility difficulties, yet there remain substantial challenges to developing wearable robots that can accommodate the needs of older adults and aid in performing daily living activities. In the current study, we developed and validated an innovative User-Centric Co-Creation (UC3) approach. First, we engaged older adults from the design phase of exoskeletons and treated them as equal partners. Second, we initiated an interactive testing platform to perform trilevel data curation, physiology, function, and behavioral level, to inform kinesiology-based parameters for wearable robots’ development. We invited a total of 16 older adults to join six co-creation workshops on wearable robots. Next, we used a multi-method pilot study using the UC3 approach to validate the interactive testing platform (N=15). After a successful pilot study and validation, a total of 157 participants were recruited in two waves. First, we recruited 91 healthy older adults aged 65 or above, between July and August 2021 to act as the reference group. Second, we invited 66 older adults with mobility difficulties between December 2021 and December 2022, who are the target users of the wearable robots. Subsequently, a total of 55 participants in the second wave joined an experiment with knee robots between May 2022 and February 2023. All the participants were invited to join experiment procedures at three levels: physiology level, function level, and behavior level. Gait motion analysis and balance ability were included at the physiology level. Maximum voluntary contraction at three knee angles (performed using knee extension test) and maximum handgrip strength were included at the function level. The Short Physical Performance Battery, a group of measures that combines the results of 4-meter walk speed, 5-time chair stand test, and balance tests, was included at the behavioral level. Following the UC3 approach, we engaged older adults as equal partners in wearable robots’ development and developed a performance-based risk hierarchy with a transdisciplinary team’s support. Prior to conducting the three-level analysis to inform the development of wearable robots, we instigated a risk hierarchy based on recommended cut-offs on handgrip strength (M: < 28 kg, F: < 18 kg), 4-metre walk speed (< 1.0 m/s), 5-time chair stand test (≥ 12 s), and SPPB total score (≤ 9). Among all the 157 participants, 29 (18.5%) were classified as having no risks, 51 (32.5%) were classified as having one risk, 29 (18.5%) were classified as having two risks, 20 (12.7%) were classified as having three risks, the remaining 28 (17.8%) were classified as having four risks. In general, we found evidence for a novel UC3 approach to inform wearable robots’ development. We started with a full engagement of target users, followed by a trilevel data curation at the physiology level, function level, and behavioral level. Lastly, continuous improvement and discussions with experts in a transdisciplinary team confirmed the validity of the UC3 approach. All in all, elucidating the unmet needs for daily activities at the physiology, function, and behavioral level will provide valuable insights into the development of intelligent wearable robots and will unlock the key to an independent living lifestyle in old age.