Store energy and save energy: Many devices have been developed to harvest energy from walking or running, but their use often comes at cost to the wearer in the form of increased metabolic demand. Shepertycky et al. designed a device that can harvest mechanical energy from a natural walking gait and convert it to useable electrical energy while also reducing the metabolic energy consumption of the user (see the Perspective by Riemer et al. ). The key to achieving “something from nothing” comes from designing the device to use muscle-centric control of the knee exoskeleton resistance to reduce active muscle force during the late part of the leg swing cycle. Science , aba9947, this issue p. 957 ; see also abh4007, p. 909

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

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.

Much research in the field of energy harvesting has sought to develop devices capable of generating electricity during daily activities with minimum user effort. No previous study has considered the metabolic cost of carrying the harvester when determining the energetic effects it has on the user. When considering device carrying costs, no energy harvester to date has demonstrated the ability to generate a substantial amount of electricity (> 5W) while maintaining a user effort at the same level or lower than conventional power generation methods (e.g. hand crank generator). We developed a lower limb-driven energy harvester that is able to generate approximately 9W of electricity. To quantify the performance of the harvester, we introduced a new performance measure, total cost of harvesting (TCOH), which evaluates a harvester’s overall efficiency in generating electricity including the device carrying cost. The new harvester captured the motion from both lower limbs and operated in the generative braking mode to assist the knee flexor muscles in slowing the lower limbs. From a testing on 10 participants under different walking conditions, the harvester achieved an average TCOH of 6.1, which is comparable to the estimated TCOH for a conventional power generation method of 6.2. When generating 5.2W of electricity, the TCOH of the lower limb-driven energy harvester (4.0) is lower than that of conventional power generation methods. These results demonstrated that the lower limb-driven energy harvester is an energetically effective option for generating electricity during daily activities.

Accurate joint angle estimation using magneto-inertial measurement units (IMU) is a significant challenge for human movement analysis outside of the lab. Sensor-to-segment misalignment and sensor orientation errors are key factors in joint angle estimation. However, the effects of these factors on 3-D knee joint angle estimation accuracy are not well understood. This paper presents a general model to investigate the effects of sensor-to-segment misalignment and IMU orientation error on 3-D knee joint angle estimation accuracy. An analytical model was built to quantify the effects of sensor-to-segment misalignment and IMU orientation errors for 3-D knee joint angle estimation. Precise outcomes were quantified via simulation analysis and experimental testing in which fourteen healthy subjects performed drop landing and cutting tasks while wearing IMUs and optical motion capture markers for ground truth estimation. Sensor-to-segment misalignment accounted for 52% of the total knee abduction angle error and 40% of the knee internal rotation angle error but did not significantly affect knee flexion angle error. Conversely, IMU orientation error accounted for 55% of the knee flexion angle error but did not significantly affect knee abduction or internal rotation angle errors. These results indicate that sensor-to-segment misalignment significantly affects knee abduction and internal rotation angle estimation while IMU orientation error significantly affects knee flexion angle estimation. This study could serve to inform the development of accurate IMU-based 3-D knee joint angle algorithms for human movement analysis.