High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators, have to compromise bandwidth to improve compliance (i.e., backdrivability). In this article, we describe the design and human-robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This article aims to corroborate the underlying philosophy of “design for control,” namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This article demonstrates performance improvement compared with state-of-the-art exoskeletons.

Human-assisting intelligent systems demand certain methods to precisely predict motorized limb joint angles. This paper presents the application of deep-recurrent neural networks (RNNs), which is a type of neural network for processing sequential data, for predicting the knee joint angle in real-time. This model is created based on a combination of electromyographic (EMG) signals, (with electrodes being placed on three leg muscles), and inertial measurements of the upper and lower legs. The data collected from different subjects when they performed different gaits were used to construct the model, which was evaluated in a real-time setting. The proposed RNN model based on fusion information contains a balance between computational complexity and prediction accuracy. Results on a microcontroller show that, within a predicted horizon of 50 ms, the model has a low prediction error of ±12.93 degrees.

Automatic gait measurement and analysis is an enabling tool for intelligent healthcare and robotics-assisted rehabilitation. This paper proposes a novel two shank-mounted inertial measurement units (IMU)-based method on gait analysis and classification for three different neurological diseases. The IMU-based gait analysis and design aims to be applied in personal daily activities and environment for remote diagnosis and rehabilitation guidance. In the design, eight spatial-temporal and kinematic gait parameters are extracted from two shank-mounted IMUs. A support vector machine-based classifier is developed to classify four types of gait patterns with different neurological diseases (healthy control, peripheral neuropathy, post-stroke and Parkinson's disease). A total of 49 subjects are recruited and 93.9% of them are assigned to the right group after the leave-one-subject-out cross validation. The results demonstrate that the proposed IMU-based gait parameters and classifier are capable of differentiating the four types of gait patterns. The analysis and design have great potentials for use in clinical applications.

Inertial measurement units (IMU) have been used for gait analysis in many clinical studies, as a more convenient, low cost and less restricted alternative to the laboratory-based motion capture systems or instrumented walkways. Spatial-temporal gait parameters such as gait cycle duration and stride length calculated from the IMUs were often used in these studies for evaluating the impaired gait. However, the spatial-temporal information provided by IMUs is limited, and sometime suffers incomplete and less effective evaluation. In this study, we develop a novel IMU-based method for clinical gait evaluation. Nine gait variables including three spatial-temporal parameters and six kinematic parameters are extracted from two shank-mounted IMUs for quantifying patient's gait deviations. Based on those parameters, an IMU-based gait normalcy index (INI) is derived to evaluate the overall gait performance. Eight inpatient subjects with gait impairments caused by n-hexane neuropathy and ten healthy subjects were recruited. The proposed gait variables and INI were examined on the inpatients at three to five time instants during the rehabilitation process until being discharged. A comparison with healthy subjects and statistical analysis for the changes of gait variables and INI demonstrated that the proposed new set of gait variables and INI can provide adequate and effective information for quantifying gait abnormalities, and help understanding the progress of gait and effectiveness of therapy during rehabilitation process.

Construction workers regularly perform tasks that require kneeling, crawling, and squatting. Working in awkward kneeling postures for prolonged time periods can lead to knee pain, injuries, and osteoarthritis. In this article, we present lightweight, wearable sensing, and knee assistive devices for construction workers during kneeling and squatting tasks. Analysis of kneeling on level and sloped surfaces (0 ^° , 10 ^° , and 20 ^° ) is performed for single- and double-leg kneeling tasks. Measurements from the integrated inertial measurement units are used for real-time gait detection and lower limb pose estimation. Detected gait events and pose estimation are used to control the assistive knee joint torque provided by lightweight exoskeletons with powerful quasi-direct drive actuation. Human subject experiments are conducted to validate the effectiveness of the proposed analysis and control design. The results show reduction in knee extension/flexion muscle activation (up to 39%) during stand-to-kneel and kneel-to-stand tasks. Knee-ground contact forces/pressures are also reduced (up to 15%) under robotic assistance during single-leg kneeling. Increasing assistive knee torque shows redistribution of the subject's weight from the knee in contact with the ground to both supporting feet. The proposed system provides an enabling intervention to potentially reduce musculoskeletal injury risks of construction workers.