Real-time Hybrid Locomotion Mode Recognition for Lower-limb Wearable Robots

Real-time recognition of locomotion-related activities is a fundamental skill that the controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities

MiRNA-200b level in peripheral blood predicts renal interstitial injury in patients with diabetic nephropathy

Background: To uncover the diagnostic potential of peripheral blood microRNA-200b (miRNA-200b) in renal interstitial injury in diabetic nephropathy (DN) patients. Methods: A total of 50 diabetes subjects, 50 mild DN subjects, 50 moderate-severe DN subjects and 50 healthy subjects were included. Peripheral blood level of miRNA-200b in every subject was detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Serum levels of renal function indicators were determined by enzyme-linked immunosorbent assay (ELISA). Meanwhile, relative levels of fibrosis damage indicators were examined by chemiluminescent immunoassay. Diagnostic potentials of miRNA200b in diabetes, mild DN and moderate-severe DN were assessed by depicting receiver operating characteristic (ROC) curves. Results: Peripheral blood level of miRNA-200b was higher in DN subjects than diabetes subjects without vascular complications, especially moderate-severe DN patients. Peripheral blood level of miRNA-200b in DN subjects was negatively correlated to relative levels of serum creatinine, urinary nitrogen, cystatin, TGF-b, CIV and PCIII. ROC curves demonstrated diagnostic potentials of miRNA-200b in mild and moderate-severe DN. Conclusions: Peripheral blood level of miRNA-200b is closely linked to the degree of renal interstitial injury in DN patients. MiRNA-200b may be a vital indicator in predicting the development of DN

Experimental validation of motor primitive-based control for leg exoskeletons during continuous multi-locomotion tasks

An emerging approach to design locomotion assistive devices deals with reproducing desirable biological principles of human locomotion. In this paper, we present a bio-inspired controller for locomotion assistive devices based on the concept of motor primitives. The weighted combination of artificial primitives results in a set of virtual muscle stimulations. These stimulations then activate a virtual musculoskeletal model producing reference assistive torque profiles for different locomotion tasks (i.e., walking, ascending stairs, and descending stairs). The paper reports the validation of the controller through a set of experiments conducted with healthy participants. The proposed controller was tested for the first time with a unilateral leg exoskeleton assisting hip, knee, and ankle joints by delivering a fraction of the computed reference torques. Importantly, subjects performed a track involving ground-level walking, ascending stairs, and descending stairs and several transitions between these tasks. These experiments highlighted the capability of the controller to provide relevant assistive torques and to effectively handle transitions between the tasks. Subjects displayed a natural interaction with the device. Moreover, they significantly decreased the time needed to complete the track when the assistance was provided, as compared to wearing the device with no assistance

Review of Assistive Strategies in Powered Lower-Limb Orthoses and Exoskeletons

Starting from the early research in the 1960s, especially in the last two decades, orthoses and exoskeletons have been significantly developed. They are designed in different architectures to assist their users’ movements. The research literature has been more prolific on lower-limb devices: a main reason is that they address a basic but fundamental motion task, walking. Leg exoskeletons are simpler to design, compared to upper-limb counterparts, but still have particular cognitive and physical requirements from the emerging human–robot interaction systems. In the state of the art, different control strategies and approaches can be easily found: it is still a challenge to develop an assistive strategy which makes the exoskeleton supply efficient and natural assistance. So, this paper aims to provide a systematic overview of the assistive strategies utilized by active locomotion–augmentation orthoses and exoskeletons. Based on the literature collected from Web of Science and Scopus, we have studied the main robotic devices with a focus on the way they are controlled to deliver assistance; the relevant validations are as well investigated, in particular experimentations with human in the loop. Finally current trends and major challenges in the development of an assistive strategy are concluded and discussed

Robotic arm-assisted total hip arthroplasty for preoperative planning and intraoperative decision-making

Abstract Aims This article aimed to explore the efficacy of robotic arm-assisted total hip arthroplasty (THA) in improving preoperative planning and intraoperative decision-making. Methods In this single-center, prospective, randomized clinical controlled trial, 60 patients were randomly divided into two groups: conventional THA (cTHA) and robotic arm-assisted THA (rTHA). The rTHA underwent procedures using a robot-assisted surgical system, which generated three-dimensional models to determine the most appropriate prosthesis size and position. The standard process of replacement was executed in cTHA planned preoperatively via X-ray by experienced surgeons. Differences between predicted and actual prosthetic size, prosthetic position, and leg length were evaluated. Results Sixty patients were included in the study, but one patient was not allocated due to anemia. No significant preoperative baseline data difference was found between the two groups. The actual versus predicted implantation size of both groups revealed that 27/30 (90.0%) in the rTHA group and 25/29 (86.2%) in the cTHA group experienced complete coincidence. The coincidence rate for the femoral stem was higher in the rTHA group (83.3%) than that in the cTHA group (62.7%). Between the actual and predicted rTHA, the difference in anteversion/inclination degree (< 6°) was largely dispersed, while cTHA was more evenly distributed in degree (< 9°). The differences in leg length between the surgical side and contralateral side showed a significant deviation when comparing the two groups (P = 0.003), with 0.281 (− 4.17 to 3.32) mm in rTHA and 3.79 (1.45–6.42) mm in cTHA. Conclusion Robotic arm-assisted total hip arthroplasty can be valuable for preoperative planning and intraoperative decision-making

Gait phase estimation based on noncontact capacitive sensing and adaptive oscillators

This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact capacitive sensing and adaptive oscillators (AOs). The capacitive sensing system is designed with two sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on capacitive sensing and AOs is a promising alternative for the control of exoskeleton/orthosis

Walking assistance using artificial primitives: A novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton

Bioinspiration in robotics deals with applying biological principles to the design of better performing devices. In this article, we propose a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. In particular, the use of motor primitives for assisting different locomotion modes (i.e., ground-level walking at several cadences and ascending and descending stairs) is explored by means of two different strategies. In the first strategy, identified motor primitives are combined through weights to directly produce the desired assistive torque profiles. In the second strategy, identified motor primitives are combined to serve as neural stimulations to a virtual model of the musculoskeletal system, which, in turn, produces the desired assistive torque profiles

A realtime locomotion mode recognition method for an active pelvis orthosis

This paper presents a realtime locomotion mode recognition method for an active pelvis orthosis. Five locomotion modes, including sitting, standing still, level-ground walking, ascending stairs, and descending stairs, are taken into consideration. The recognition is performed with locomotion information measured by the onboard hip angle sensors and the pressure insoles. These five modes are firstly divided into static modes and dynamic modes, and the two kinds are classified by monitoring the variation of the relative hip angles of the two legs within a pre-defined period. Static states are further classified into sitting and standing still based on the absolute hip angle. As for dynamic modes, a fuzzy-logic based method is proposed for the recognition. Two event-based locomotion features, including the hip joint angle at the first foot-strike and the center of foot pressure at the first foot-strike are used to calculate the membership of different modes based on the membership function, and the mode with the maximal membership is selected as the target mode. Experimental results with three subjects achieve an average recognition accuracy of 99.87% and average recognition delay of 18.12% of one gait cycle