Purpose-centered design of rehabilitation robots: a case study of a hand exoskeleton for assessing spasticity

Hand exoskeletons offer promising solutions for hand function rehabilitation. Despite considerable research on novel exoskeleton technologies and the commercial availability of many hand exoskeletons, most hand exoskeletons can only perform motor training and assistance, rather than treatment or diagnosis of hand diseases. This limitation is also observed in other rehabilitation robots. Designing a rehabilitation robot for medical purposes necessitates the integration of existing technologies, tailored to the symptoms, clinical conditions, and management methods of the target disease. To facilitate such design, this study presents a design strategy for rehabilitation robots that assist in the therapy and assessment of specific diseases. The strategy first systematically gathers prior medical and engineering knowledge on the management of the target disease and clarifies all design requirements, including medical goals and user needs. Next, modified Quality Function Deployment (QFD) and Theory of Inventive Problem Solving (TRIZ) methods are used to identify the primary requirements and the optimal design scheme based on existing technical solutions. Lastly, the optimal design scheme is prototyped, tested, and refined iteratively until the product satisfies the design purpose. As an illustrative case study of this design strategy, the design of a hand exoskeleton for assessing hand spasticity is discussed. This strategy is adaptable for designing hand exoskeletons for other diseases or developing other types of rehabilitation robots, thereby extending the potential of rehabilitation robots in diagnostic and therapeutic applications

Differentiated responses of daytime and nighttime sap flow to soil water deficit in a larch plantation in Northwest China

Tree transpiration contributes to the forest water budget and its environmental responses differ between daytime and nighttime. Elucidating these divergent responses separately from their biophysical controls is crucial for ascertaining the forest-water relationship in changing environments. To investigate the biophysical controls on daytime and nighttime sap flow (Qd and Qn), the sap flux density, meteorological conditions, soil moisture, and canopy leaf area index (LAI) were simultaneously monitored in a Larix principis-rupprechtii plantation in the Liupan Mountains of Northwest China during the growing seasons (June to September) of three hydrological years (2019, 2021, and 2022). The results showed that the main control factors for Qd and Qn varied depending on the timescale. The variation in Qd was mainly controlled by daytime solar radiation (Rd), vapour pressure deficit (VPDd), relative extractable soil water (REWd) and LAI at a daily scale, but only by Rd at a monthly scale and only by VPDd at an interannual scale. The variation in Qn was mainly controlled by the nighttime vapour pressure deficit (VPDn) and relative extractable soil water (REWn), LAI, and Qd at a daily scale but only by VPDn at monthly and interannual scales. The responses of Qd and Qn to soil water deficit were different and varied by timescale. At the daily scale, the effect of VPDd on Qd became weak, while the effect of VPDn on Qn became strong in drier years (2021 and 2022). At the monthly scale, as the main controlling factor of the monthly variation in Qd (Qn), Rd (VPDn) played a decisive role in the wet (dry) months. At the interannual scale, VPD limited Qd but drove Qn, leading to a decrease in Qd and an increase in Qn with increasing soil water deficit. Overall, our findings revealed the different responses of Qd and Qn to biophysical factors and underscored that Qd and Qn should be explored both separately and synchronously; additionally, our results indicated that the proportion of Qn might increase if soil water stress is increased due to future climate change

Purpose-centered design of rehabilitation robots: a case study of a hand exoskeleton for assessing spasticity

Hand exoskeletons offer promising solutions for hand function rehabilitation. Despite considerable research on novel exoskeleton technologies and the commercial availability of many hand exoskeletons, most hand exoskeletons can only perform motor training and assistance, rather than treatment or diagnosis of hand diseases. This limitation is also observed in other rehabilitation robots. Designing a rehabilitation robot for medical purposes necessitates the integration of existing technologies, tailored to the symptoms, clinical conditions, and management methods of the target disease. To facilitate such design, this study presents a design strategy for rehabilitation robots that assist in the therapy and assessment of specific diseases. The strategy first systematically gathers prior medical and engineering knowledge on the management of the target disease and clarifies all design requirements, including medical goals and user needs. Next, modified Quality Function Deployment (QFD) and Theory of Inventive Problem Solving (TRIZ) methods are used to identify the primary requirements and the optimal design scheme based on existing technical solutions. Lastly, the optimal design scheme is prototyped, tested, and refined iteratively until the product satisfies the design purpose. As an illustrative case study of this design strategy, the design of a hand exoskeleton for assessing hand spasticity is discussed. This strategy is adaptable for designing hand exoskeletons for other diseases or developing other types of rehabilitation robots, thereby extending the potential of rehabilitation robots in diagnostic and therapeutic applications

Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows

This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow based on the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subjects

Exploring the Impact of Planetary Boundary Layer Schemes on Rainfall Forecasts for Typhoon Mujigae, 2015

Sensitivity experiments were conducted on Typhoon Mujigae, which occurred in 2015, wherein the Weather Research and Forecasting Advanced Research (WRF-ARW) model was used to select two local and two nonlocal planetary boundary layer (PBL) parameterization schemes: the quasi-normal scale elimination (QNSE) and Mellor–Yamada–Janjic (MYJ) schemes, and the Yonsei University (YSU) and medium-range forecast (MRF) schemes, respectively. The differences in rainfall response in the typhoon’s inner core and outer region were evaluated by comparing the anomaly rainfall distribution, heat transmission, and mixing processes in the boundary layer among the PBL schemes. The results show that the simulated rainfall in typhoon Mujigae has large uncertainty among the PBL schemes and a significant difference between the inner and outer regions. Compared with the observation, the simulated rainfall was significantly higher in the inner core and slightly lower in the outer region. All PBL schemes accurately identified the rainfall location, although the amounts differed between the schemes. The rainfall levels in the MRF scheme were closest to the observation, followed by those in the YSU and MYJ schemes; the QNSE scheme showed the largest deviation. In general, rainfall simulation using a nonlocal boundary layer scheme such as MRF had the best results for both the inner core and the outer region