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Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image-based robot system. Methods Robot-assisted ACL reconstruction was performed on twelve freshly frozen knee specimens. During the operation, three-dimensional images were used for ACL bone tunnel planning, and the robotic arm was used for navigation and drilling. Twelve patients who underwent traditional arthroscopic ACL reconstruction were included. 3D computed tomography was used to measure the actual position of the ACL bone tunnel and to evaluate the accuracy of the robotic and traditional ACL bone tunnel. Results On the femoral side, the positions of robotic and traditional surgery tunnels were 29.3 ± 1.4% and 32.1 ± 3.9% in the deep-to-shallow direction of the lateral femoral condyle (p = 0.032), and 34.6 ± 1.2% and 21.2 ± 9.4% in the high-to-low direction (p < 0.001), respectively. On the tibial side, the positions of the robotic and traditional surgical tunnels were located at 48.4 ± 0.9% and 45.8 ± 2.8% of the medial-to-lateral diameter of the tibial plateau (p = 0.008), 38.1 ± 0.8% and 34.6 ± 6.0% of the anterior-to-posterior diameter (p = 0.071), respectively. Conclusions In this study, ACL reconstruction was completed with the assistance of a robot arm and 3D images, and the robot was able to drill the bone tunnel more accurately than the traditional arthroscopic ACL reconstruction., (© 2024. The Author(s).)

The co-located and concurrent measurement of both muscular neural activity and muscular deformation is considered necessary in many applications, such as medical robotics, assistive exoskeletons and muscle function evaluations. Nevertheless, conventional muscle-related signal perception systems either detect only one of these modalities, or are made with rigid and bulky components that cannot provide conformal and flexible interface. Herein, a flexible, easy-to-fabricate, bimodal muscular activity sensing device, which collects neural and mechanical signal at the same muscle location, is reported. The sensing patch includes a screen-printed sEMG sensor, and a pressure-based muscular deformation sensor (PMD sensor) based on a highly sensitive, co-planar iontronic pressure sensing unit. Both sensors are integrated on a super-thin (25 μm) substrate. The sEMG sensor shows a high signal-to-noise ratio of 37.1 dB, and the PMD sensor sensor exhibits a high sensitivity of 70.9 kPa -1 . The responses of the sensor to three types of muscle activities (isotonic, isometric, and passive stretching) were analyzed and validated by ultrasound imaging. Bimodal signals during dynamic walking experiments with different level-ground walking speeds were also investigated. The application of the bimodal sensor was verified in gait phase estimation, and results show that the assembly of both modalities significantly reduce (p < 0.05) the average estimation error across all subjects and all walking speeds to 3.82%. Demonstrations show the potential of this sensing device for informative evaluation of muscular activities, and its abilities in human-robot interaction.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

Background: The combination of navigational system and robotics has the potential to accurately identify and drill bone tunnels in anterior cruciate ligament (ACL) reconstruction. This study explores the feasibility and accuracy of bone tunnel positioning using the TiRobot, an orthopaedic surgical robot., Methods: The experiment was divided into two groups. In group A, the bone tunnels were positioned using the TiRobot surgical robot (n = 8). In group B, handheld locators were used for positioning (n = 8)., Results: TiRobot can be used for positioning the ACL bone tunnel. The accuracy of positioning the femoral tunnel in group A and B was 1.00 ± 0.20 and 3.10 ± 0.59 mm, respectively (t = -9.49, P < 0.001). As for tibial tunnel, the accuracy was 1.02 ± 0.20 and 2.64 ± 0.14 mm, respectively (t = -18.54, P < 0.001)., Conclusions: The bone tunnel drilling precision using TiRobot for ACL reconstruction surgery was more accurate than traditional surgical techniques., (© 2021 John Wiley & Sons Ltd.)

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics.

Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features of on-prosthesis mechanical signals in inter-session and inter-day recognition tasks. The strategy was designed with an automatic training algorithm which could update the classifiers with the data of the most recent completed gait cycles to seize the changes of the features brought by external disturbances. After implementation, we measured multiple experimental sessions on six transtibial amputees with intervals from a few hours (inter-session experiment) to several months (108 days at most in inter-day experiment). In each session, they performed five locomotion modes and eight locomotion transitions using the robotic prosthesis. Between each two experimental sessions, the subjects were required to doff the robotic prosthesis (including the socket). The proposed adaptive recognition algorithm significantly improved recognition accuracies in both experiments. In inter-session experiment, the proposed method increased the recognition accuracy from 89.3% to 92.8% than previous non-adaptive recognition method. In inter-day experiments, it increased the recognition accuracy from 60% to 88.8%. If taking three modes (level walking, stair ascending/descending) and four locomotion transitions into calculation, the recognizer produced an accuracy up to 96.6% (swing phase) for static mode and an accuracy of 96.9% for locomotion transitions on inter-day tasks without manual intervenes. Compared with state-of-the-art, our study extends the ability of the robotic transtibial prosthesis in locomotion mode recognition across multiple sessions and days. Future efforts are worth being paid in this direction to get more promising results.

Human intent recognition is important to the control of robotic prosthesis. In this paper, we propose a multi-level real-time on-board system to recognize continuous locomotion modes. A cascaded classification strategy is designed for the recognition of six steady locomotion modes and 10 transitions. On-board signals of the robotic prosthesis include two inertial measurement units and one load cell. Three transtibial amputees are recruited in the experiments. The prediction decision time of the real-time on-board cascaded classification system is about 3.3 ms, which is enough short compared with the sliding window increment 10 ms. It is easy to recognize the standing and ambulation in the first-level classification with a 99.86% accuracy by quadratic discriminant analysis (QDA) classifier. In the second-level classification, threshold method is adopted to divide one stride into swing and stance phases. In swing phase, five steady modes are recognized with a total accuracy of 96.40%. In stance phase, all these five steady modes are recognized with a total accuracy of 91.21%. The average recognition accuracy of the three subjects is 93.21% by QDA classifier. Besides, for transitions, the proposed system could recognize all transitions rightly. The designed system is feasible and effective to realize real-time on-board recognition of continuous locomotion modes, which is promising for the further control of the prosthesis.

Affording lower-limb amputees the ability to volitionally control robotic prostheses can improve the adaptability to terrain changes as well as enhancing proprioception. However, it also increases amputees' conscious burdens for prosthesis control. Therefore, in this paper, we aim to propose a hybrid controller which combines human volitional control with the intrinsic controller on the robotic transtibial prosthesis, enabling the amputee actively controlling prosthesis with little conscious attention. In this preliminary study, a hybrid controller for adaptive slope walking was designed. A slope estimator was embedded in the intrinsic controller to estimate the ground slope of the previous step using signals measured by prosthetic sensors. And a myoelectric controller allows the amputee subject to convey slope changes to prosthetic controller by volitionally contract his residual muscles, whose electromyography signals were mapped to the slope increment. The hybrid controller combined these two results to obtain the estimated slope. One male transtibial amputee subject was recruited in this research. Experiment results showed that the intrinsic slope estimator produced satisfactory estimation results with an average absolute error of 0.70 ± 0.54 degrees. By adding amputee's volitional control, the hybrid controller is able to predict the upcoming slope changes.

Walking behavior is modulated by controlling joint torques in most existing passivity-based bipeds. Controlled Passive Walking with adaptable stiffness exhibits controllable natural motions and energy efficient gaits. In this paper, we propose torque-stiffness-controlled dynamic bipedal walking, which extends the concept of Controlled Passive Walking by introducing structured control parameters and a bio-inspired control method with central pattern generators. The proposed walking paradigm is beneficial in clarifying the respective effects of the external actuation and the internal natural dynamics. We present a seven-link biped model to validate the presented walking. Effects of joint torque and joint stiffness on gait selection, walking performance and walking pattern transitions are studied in simulations. The work in this paper develops a new solution of motion control of bipedal robots with adaptable stiffness and provides insights of efficient and sophisticated walking gaits of humans.

Summary: For a real-time robotic prosthetic control, gait event detection plays an important role. In this paper, one novel sensor was proposed to realize gait event detection. The sensor includes one strain gauge bridge, which can reflect the entire deformation of carbon-fiber footplate on a robotic prosthesis. Three unilateral transtibial amputees participated in the experiments. Experimental results show that using the proposed sensor method, gait event detection (stance phase and swing phase) accuracy is approximately 100%. Based on the detected gait events, three locomotion modes (sit, stand, and walk) and the corresponding transition modes could be determined. Difference between different gait event detection systems was further conducted. [ABSTRACT FROM AUTHOR]

While initially conceived for human motion augmentation, wearable robots have gradually evolved as technological aids in motion assistance and rehabilitation. There are increasing real-world applications in industrial and medical scenarios. Though efforts have been made on wearable robotic systems, e.g. robotic prostheses and exoskeletons, there are still several challenges in kinematics and actuation solutions, dynamic analysis and control of human-robot systems, neuro-control and human-robot interfaces; ergonomics and human-in-the-loop optimization. Meanwhile, real-world applications in industrial or medical scenarios are facing difficulties considering effectiveness. [ABSTRACT FROM AUTHOR]

Recent advancement of robotic transtibial prostheses can restore human ankle dynamics in different terrains. Automatic locomotion transitions of the prosthesis guarantee the amputee's safety and smooth motion. In this paper, we present a noncontact capacitive sensing-based approach for recognizing locomotion transitions of amputees with robotic transtibial prostheses. The proposed sensing system is designed with flexible printed circuit boards which solves the walking instability brought by our previous system when using robotic prosthesis and improves the recognition performance. Six transtibial amputees were recruited and performed tasks of ten locomotion transitions with the robotic prosthesis that we recently constructed. The capacitive sensing system was integrated on the prosthesis and worked in combination with on-prosthesis mechanical sensors. With the cascaded classification method, the proposed system achieved 95.8% average recognition accuracy by support vector machine (SVM) classifier and 94.9% accuracy by quadratic discriminant analysis (QDA) classifier. It could accurately recognize the upcoming locomotion modes from the stance phase of the transition steps. In addition, we proved that adding capacitance signals could significantly reduce recognition errors of the robotic prosthesis in locomotion transition tasks. Our study suggests that the fusion of capacitive sensing system and mechanical sensors is a promising alternative for controlling the robotic transtibial prosthesis. [ABSTRACT FROM AUTHOR]

A robotic brace based on 3-R P R parallel mechanism has been proposed to assist trunk moving and train the trunk core muscle groups in our early work. The mechanical performance of the robotic brace affects the wearer's activities and comfort in clinical application, which is determined by the structural parameters of the parallel mechanism. We tried to seek the optimal structural parameters using an improved genetic algorithm, where the workspace and singularity are regarded as optimization objectives. Two prototypes with generic and optimal structural parameters were built to analyze performance improvement. Force control experiments were conducted under three postures including left lateral bending, natural, and right lateral bending. The results show that the prototype with optimal structural parameters has lower energy consumption, closer magnitude of the driving current, and a lower driving current for each linear actuator. Furthermore, an improved robotic brace was designed based on the optimal structural parameters and the feasibility was validated through the wearing experiment. [ABSTRACT FROM AUTHOR]

Ankle joint with spasticity and/or contracture can severely disable the mobility and the independence of stroke survivors. In this paper, we developed a proprioceptive neuromuscular facilitation (PNF) integrated robotic ankle–foot system for post stroke rehabilitation. The system consists of a robotic platform and a control system with graphic user interface. We employ five normal subjects to test the reliability and feasibility of the proposed system. To validate the effectiveness of the PNF integrated robotic system, we recruit five stroke patients and carry out a six-week PNF treatment. Treatment outcome was evaluated quantitatively in passive and active joint properties. The passive hysteresis loop shows that the maximum dorsiflexion angle increases from 32.9 ° ± 1.5 ° to 42.0 ° ± 3.2 ° ( p = 0.014 ) while the resistance torque decreases from 45.6 Nm±5.8 N m to 29.8 N m±4.4 N m ( p = 0.019 ). The active joint properties are improved significantly with the training score increasing from 5.7 ± 0.9 to 8.1 ± 0.6 , and getting close to that of normal subjects ( 9.5 ± 0.3 ). In addition, muscle strength has a rising trend as time goes on. The results demonstrate that the proposed PNF integrated robotic ankle–foot rehabilitation system is effective in improving ankle spasticity and/or contracture and is a promising solution in clinical rehabilitation. [ABSTRACT FROM AUTHOR]