Sci Robot

Robotic leg prostheses promise to improve the mobility and quality of life of millions of individuals with lower-limb amputations by imitating the biomechanics of the missing biological leg. Unfortunately, existing powered prostheses are much heavier and bigger and have shorter battery life than conventional passive prostheses, severely limiting their clinical viability and utility in the daily life of amputees. Here, we present a robotic leg prosthesis that replicates the key biomechanical functions of the biological knee, ankle, and toe in the sagittal plane while matching the weight, size, and battery life of conventional microprocessor-controlled prostheses. The powered knee joint uses a unique torque-sensitive mechanism combining the benefits of elastic actuators with that of variable transmissions. A single actuator powers the ankle and toe joints through a compliant, underactuated mechanism. Because the biological toe dissipates energy while the biological ankle injects energy into the gait cycle, this underactuated system regenerates substantial mechanical energy and replicates the key biomechanical functions of the ankle/foot complex during walking. A compact prosthesis frame encloses all mechanical and electrical components for increased robustness and efficiency. Preclinical tests with three individuals with above-knee amputation show that the proposed robotic leg prosthesis allows for common ambulation activities with close to normative kinematics and kinetics. Using an optional passive mode, users can walk on level ground indefinitely without charging the battery, which has not been shown with any other powered or microprocessor-controlled prostheses. A prosthesis with these characteristics has the potential to improve real-world mobility in individuals with above-knee amputation.R01 HD098154/HD/NICHD NIH HHSUnited States/T42 OH008414/OH/NIOSH CDC HHSUnited States

MECHANICAL IMPEDANCE OF ANKLE AS A FUNCTION OF ELECTROMYOGRAPHY SIGNALS OF LOWER LEG MUSCLES USING ARTIFICIAL NEURAL NETWORK

This paper reports on the feasibility of developing a model to describe the nonlinear relationship between the mechanical impedance of the human ankle within a specified range of frequency and the root mean square (RMS) value of the Electromyography (EMG) signals of the muscles of human ankle using Artificial Neural Network (ANN). A lower extremity rehabilitation robot — Anklebot was used to apply pseudo-random mechanical perturbations to the ankle and measure the angular displacement of the ankle to estimate the data of ankle mechanical impedance. Meanwhile, the surface EMG signals from the selected muscles were monitored and recorded using a Delsys Trigno® system. The final ANN models in this paper were created in two degrees of freedom — dorsiflexion-plantarflexion (DP) and inversioneversion (IE) at 3 different muscle activation levels. The results of analysis of the ANN model showed the feasibility of developing models with adequate accuracy and to define the mechanical impedance of the human ankle in terms of lower extremity muscles’ EMG statistical properties

Application of wearable sensors in actuation and control of powered ankle exoskeletons: a Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided