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Knee rehabilitation therapy after trauma or neuromotor diseases is fundamental to restore the joint functions as best as possible, exoskeleton robots being an important resource in this context, since they optimize therapy by applying tailored forces to assist or resist movements, contributing to improved patient outcomes and treatment efficiency. One of the points that must be taken into account when using robots in rehabilitation is their interaction with the patient, which must be safe for both and guarantee the effectiveness of the treatment. Therefore, the objective of this study was to assess the interaction between humans and an exoskeleton during the execution of knee flexion–extension movements under various configurations of robot assistance and resistance. The evaluation encompassed considerations of myoelectric activity, muscle recruitment, robot torque, and performed movement. To achieve this, an experimental protocol was implemented, involving an individual wearing the exoskeleton and executing knee flexion–extension motions while seated, with the robot configured in five distinct modes: passive (P), assistance on flexion (FA), assistance on extension (EA), assistance on flexion and extension (CA), and resistance on flexion and extension (CR). Results revealed distinctive patterns of movement and muscle recruitment for each mode, highlighting the complex interplay between human and robot; for example, the largest RMS tracking errors were for the EA mode (13.72 degrees) while the smallest for the CR mode (4.47 degrees), a non-obvious result; in addition, myoelectric activity was demonstrated to be greater for the completely assisted mode than without the robot (the maximum activation levels for the vastus medialis and vastus lateralis muscles were more than double those when the user had assistance from the robot). Tracking errors, muscle activations, and torque values varied across modes, emphasizing the need for careful consideration in configuring exoskeleton assistance and resistance to ensure effective and safe rehabilitation. Understanding these human–robot interactions is essential for developing precise rehabilitation programs, optimizing treatment effectiveness, and enhancing patient safety.

Knee orthosis plays a vital role in enhancing the wellbeing and quality of life of individuals suffering from knee arthritis. This study explores a machine-learning-based methodology for predicting a user’s gait subphase using inertial measurement units (IMUs) for a semiactive orthosis. A musculoskeletal simulation is employed with the help of existing experimental motion-capture data to obtain essential metrics related to the gait cycle, which are then normalized and scaled. A meticulous data capture methodology using foot switches is used for precise synchronization with IMU data, resulting in comprehensive labeled subphase datasets. The integration of simulation results and labeled datasets provides activation data for effective knee flexion damping following which multiple supervised machine learning algorithms are trained and evaluated for performances. The time series forest classifier emerged as the most suitable algorithm, with an accuracy of 86 percent, against randomized convolutional kernel transform, K-neighbor time series classifier, and long short-term memory–fully convolutional network, with accuracies of 68, 76, and 78, respectively, showcasing exceptional performance scores, thereby rendering it an optimal choice for identifying gait subphases and achieving the desired level of damping for magnetorheological brake-mounted knee orthosis based on simulated results.

Knee osteoarthritis (OA) is a degenerative condition that critically affects locomotor ability and quality of life and, the condition is particularly prevalent in the senior population. The current review presents a gait biomechanics conceptual framework for designing active knee orthoses to prevent and remediate knee OA. Constant excessive loading diminishes knee joint articular cartilage and, therefore, measures to reduce kinetic stresses due to the fact of adduction moments and joint compression are an essential target for OA prevention. A powered orthosis enables torque generation to support knee joint motions and machine-learning-driven “smart systems” can optimise the magnitude and timing of joint actuator forces. Although further research is required, recent findings raise the possibility of exoskeleton-supported, non-surgical OA interventions, increasing the treatment options for this prevalent, painful and seriously debilitating disease. Combined with advances in regenerative medicine, such as stem cell implantation and manipulation of messenger ribonucleic acid (m-RNA) transcription, active knee orthoses can be designed to incorporate electro-magnetic stimulators to promote articular cartilage resynthesis.

Semi-active knee orthosis (SAKO) is a kind of wearable lower-limb exoskeleton that uses actuators to support the regular biomechanical functions. It is much better than conventional knee orthosis (CKO) devices because of its high torque to volume ratio (TVR) and low mass. Magnetorheological (MR) brake is one of the smart actuators that can be used as an active resistance device in SAKO. It has advantages of fast response, low power consumption, and low vibration operation. This smart brake also has wide applications in the robotic and automotive industries. However, the electromagnetic setup in MR brakes has a hysteresis problem. This paper aims to turn this hysteresis problem into an advantage to save the power consumption of MR brake. Since the SAKO needs precise torque control, this research studied the hysteresis effect on the torque performance of MR brake. A less energy-consuming PWM actuation signal is proposed to activate the MR brake. The effects of frequency and duty cycle of PWM actuation signal on MR brake performance are also investigated. The electromagnetic (EM) and mechanical models of the MR brake were developed to simulate performance. Initial validation of these models is done by simulating the MR brake model with the DC actuation signal in finite element analysis software. For the final validation, the model simulation results are compared with experimental results. The factors affecting the steady torque and the response time of the MR brake are studied to find the optimal frequency and duty cycle for the applied PWM signal. This study revealed that the proposed new PWM actuation signal with a 5 kHz frequency and 60% duty cycle can power the MR brake to maintain steady torque. By turning hysteresis into an advantage, it saves 40% power consumption of MR brake compared to DC signal.