Robotic Knee Research Articles

Muscle-Driven Total Knee Replacement Stability with Virtual Ligaments

Knee joint stability comprises passive (ligaments), active (muscles), and static (articular congruency) contributors. The stability of total knee replacement (TKR) implants can be assessed pre-clinically using joint motion simulators. However, contemporary testing methods with these platforms do not accurately reproduce the biomechanical contributions of passive stabilizers, active stabilizers, or both. A key component of joint stability is therefore missing from laxity tests. A recently developed muscle actuator system (MAS) pairs the quadriceps-driven motion capabilities of an Oxford knee simulator with the prescribed displacements and laxity testing methods of a VIVO robotic knee testing system, which also includes virtual ligament capabilities. Using a TKR-embedded non-cadaveric joint analogue, TKR with two different virtual ligament models were compared to TKR with no active ligaments. Laxity limits were then obtained for both developed models using the conventional style of laxity testing (the VIVO’s force/displacement control) and compared with results obtained under similar conditions with the MAS (gravity-dependent muscle control). Differences in joint control methods identified the need for muscle forces providing active joint stability, while differences in the effects of the virtual ligament models identified the importance of physiological representations of collateral ligaments during testing.

Initial Experience With VELYS Robot-Assisted Total Knee Replacement: Coronal Plane Accuracy and Effect of Robotic Training on Outcomes.

The use of robots for arthroplasty is gaining momentum inrecent times to provide accuracy in bony cuts and alignment. We aimed to study the efficacy of coronal plane correction with a new robotic system (VELYS™Robotic-Assisted Surgery) and also the effect of the learning curve of robot-assisted total knee arthroplasty (RATKA) on outcomes. We hypothesize that the benefits of RATKA are not limited to only surgeons having specific training in robotic knee replacement. A total of 101 RATKAs were performed between November 1, 2022, andDecember 1, 2022, by a surgeon and all the cases were included in this study. The first 50 consecutive knees were considered as 'Cohort I' and the next 51 consecutive knees as 'Cohort II'. The intraoperative robotic registration data and tourniquet time were recorded. On three months follow‑up, Oxford Knee Score and lower limb scannogram were recorded. All the 101 cases achieved the desired coronal plane alignment within 3 degrees from neutral. There was a significant difference in the tourniquet time between the two groups. There was no significant difference in the mean three months post-operative values of coronal and sagittal deformity correction, range of flexion, and Oxford Knee Score between the two groups. The VELYS™​​​​​​​robot-assisted system produces an accurate correction of coronal alignment. As the surgeon's experience increases with the system, there is a reduction in tourniquet time; however, the degree of deformity correction is comparable to that when he had no experience. Hence the benefits of RATKA are not limited to only surgeons having specific training in robotic-assisted knee replacement.

Early Learning Curve in Robotic-Assisted Total Knee Arthroplasty: A Single-Center Experience.

Background/Objectives: This study evaluated the learning curve for robotic-assisted total knee arthroplasty (RA TKA) performed by three experienced surgeons, focusing on procedure duration, surgeon satisfaction, and confidence. Methods: A prospective study was conducted with three senior arthroplasty surgeons, each performing 15 RA TKA procedures using the Triathlon Knee System with the Robotic Arm Interactive Orthopedic (RIO) System. Data on preparation, cut-to-suture, and breakdown times were collected. Surgeon anxiety levels were measured preoperatively using the STAI-6 scale, while postoperative satisfaction and confidence were assessed via a questionnaire. Statistical analysis was conducted using GraphPad Prism. Results: Of 50 scheduled surgeries, 45 were completed. The average cut-to-suture time was 1 h 38 min, with significant time reductions in robotic-specific steps as experience increased. Comparing the first five surgeries to the last five, the time for navigation hardware mounting, landmarks registration, femur and tibia registration, and bone preparation decreased by up to 30% (p < 0.001 to p = 0.025). General instrument preparation time decreased by 20% (p = 0.004). Surgeon anxiety levels dropped, indicating increased comfort with the system, while postoperative surveys showed increased satisfaction and confidence. Conclusions: The study demonstrated a substantial learning curve for RA TKA, with improved efficiency and surgeon confidence by the fifteenth procedure. These findings highlight the potential for streamlined workflows and guide training for new adopters of robotic knee arthroplasty.

A deep learning-based comprehensive robotic system for lower limb rehabilitation

In the modern era, a significant percentage of people around the world suffer from knee pain-related problems. ‘Knee pain’ can be alleviated by performing knee rehabilitation exercises in the correct posture on a regular basis. In our research, an attention mechanism-based CNN-TLSTM (Convolution Neural Network-tanh Long Sort-Term Memory) network has been proposed for assessing the knee pain level of a person. Here, electroencephalogram (EEG) signals of the frontal, parietal, and temporal lobes, electromyography (EMG) signals of the hamstring and quadriceps muscles, and knee bending angle have been used for knee pain detection. First, the CNN network has been utilized for automated feature extraction from the EEG, knee bending angle, and EMG data, and subsequently, the TLSTM network has been used as a classifier. The trained CNN-TLSTM model can classify the knee pain level of a person into five categories, namely no pain, low pain, medium pain, moderate pain, and high pain, with an overall accuracy of 95.88 %. In the hardware part, a prototype of an automated robotic knee rehabilitation system has been designed to help a person perform three rehabilitation exercises, i.e., sitting knee bending, straight leg rise, and active knee bending, according to his/her pain level, without the presence of any physiotherapist. The novelty of our research lies in (i) designing a novel deep learning-based classifier model for broadly classifying knee pain into five categories, (ii) introducing attention mechanism into the TLSTM network to boost its classification performance, and (iii) developing a user-friendly rehabilitation device for knee rehabilitation.

Is bicortical femoral pin insertion safe for Image-based Robotic Knee Arthroplasty Surgery ? A comparative complications analysis in 970 Consecutive Cases

ObjectivesLimited data exists on complications associated with robotic image-based system in knee arthroplasty. This study aims to document complications in robotic arm-assisted knee arthroplasties, and evaluate the system’s safety by comparing two femoral pin insertion methods: bicortical diaphyseal with additional stab wounds, and unicortical metaphyseal placement through the main incision. MethodsAll patients undergoing primary knee arthroplasty with the image-based robotic system (Mako, Stryker, Mako Surgical Corp., Fort Lauderdale, FL, USA) from 1st March 2021 to 31st January 2024 with a minimum follow-up of 2 months were included. Demographics, system and non-system-related complications, as well as outcomes were recorded. Complications were categorized as either major (requiring a second surgical intervention) or minor. ResultsA total of 970 consecutive cases (median age 69.3 years) were analyzed. The unicortical group comprised 651 cases, while the bicortical group 319. The incidence of non-system-related complications was 2.37%, with the most common being joint stiffness (10 cases; 1.03%), followed by lateral femoral condyle fracture (4;0.41%). The overall incidence of system-specific complications was 1.03%. Pin-related femoral fractures occurred in 0.2% of cases, all postoperatively and in the unicortical group. There was no statistically significant difference between the femoral pin insertion-related complication rates among the two groups (0.3% in the unicortical, compared to 0% in the bicortical group; p-value= 0.3). Complications included tibia fracture (0.1%), delayed wound healing (0.2%), superficial wound infection (0.1%), tibia osteomyelitis (0.1%), and “exostosis” (0.2%). The major complications rate was 0.3% and minor 0.7%. ConclusionsMinimal system-specific overall complications indicate that robotic arm-assisted surgery is safe. The bicortical diaphyseal femoral pin insertion method does not increase the complication rates compared to the unicortical metaphyseal method. Level of evidenceIII

Why does an implant-agnostic approach to robotic knee surgery make sense now?

The author reviews the concepts behind an implant-agnostic approach to robotic knee surgery.

An observational study on the functional outcomes of 100 robotic total knee replacements performed by an Indian surgeon: Early experiences

Abstract Background: We aim to share our preliminary encounter with robotic knee replacements in Indian patients, focusing on the initial 100 cases. This report will delve into our observations regarding the learning curve and provide insights from a short-term follow-up at 6 months. Materials and Methods: Following institutional ethical clearance, we conducted an observational study involving 100 robotic-assisted total knee replacement cases performed from March 2022 to November 2022. These patients underwent a 6-month follow-up to evaluate functional outcomes. Our primary objective was to investigate the learning curve associated with robotic-assisted total knee arthroplasty (RATKA), specifically emphasizing operative time. Furthermore, we examined preoperative and postoperative alignment parameters as part of our analysis. Results: In our investigation, participants had a mean age of 64.68 years, with a female-to-male ratio of 43:22 and an average body mass index of 30.71 ± 4.88. Intraoperative blood loss was recorded at an average of 91.81 ± 21.63 mL. The mean surgical duration for the initial 25 cases was 102.5 ± 11.08 min, which improved to 65.65 ± 13.07 min for the subsequent 75 cases. The average hospital stay length was 2.88 ± 0.88 days. Notably, there was a significant enhancement in the Knee Society Score (KSS), with a preoperative mean of 38.90 ± 12.72 improving to a postoperative mean of 84.89 ± 4.10 (P = 0.001), as well as in the Oxford Knee Score (OKS), which saw a rise from 16.62 ± 16.42 preoperatively to 45.58 ± 4.97 postoperatively (P = 0.001). The accuracy of preoperative determination for femoral component sizes was 100%, while for tibial component sizing, it was 97%. Conclusion: The key finding from this observational study suggests that around 25 procedures are required to reach a learning plateau. During this process, an average decrease of 36.1 min in surgical time was observed from the initial to proficient stages. Notably, no learning curve was observed for lower limb alignment and implant placement, and no significant complication rates were documented.

Robotic Total Knee Arthroplasty is Associated with Earlier Return of Postoperative Range of Motion.

Postoperative range of motion (ROM) is an important measure for the functional outcome and overall success after total knee arthroplasty (TKA). While robotic knee systems have been shown to reduce pain and improve early function, the return of postoperative ROM specifically has not been adequately studied. The purpose of this study was to compare postoperative ROM in robotic and conventional TKA. We hypothesized that robotic TKA leads to an improvement in postoperative ROM. A retrospective cohort study of 674 primary TKAs by a single surgeon between January 2018 and February 2023 was completed. Patients that did not have both a two-week follow up and eight-week follow up were excluded. Revision/conversion TKAs were excluded. The population was divided into two cohorts based on technique utilized: robotic versus conventional. Preoperative extension/flexion data, postoperative extension/flexion data at two-week and eight-week follow ups, and manipulation under anesthesia data were collected. ROM was defined as flexion minus extension. Chi-square tests were used to examine for differences between categorical variables and t-tests for continuous variables. A total of 307 robotic and 265 conventional knees were included. There were no differences in demographics, mean follow up, or preoperative ROM between groups. The robotic group had significantly more flexion (99.20° vs. 96.98°; p=0.034) and ROM (97.81° vs. 95.56°; p=0.047) at the two-week follow up. The loss in ROM at the two-week follow up from preoperative ROM was significantly less for the robotic group (-11.21° vs. -14.16°; p=0.031). There were no significant differences in extension at either follow up, in flexion at the eight-week follow up, or in ROM at the eight-week follow up. Robotic TKA leads to an improvement in postoperative flexion and ROM when compared to preoperative ROM at two-week follow up. These findings could partially explain the quicker recovery associated with robotic TKA.

Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials

ObjectivesRobotic knee arthroplasty procedures have emerged as a new trend, garnering attention from orthopedic surgeons globally. It has been hypothesized that the use of robotics enhances the accuracy of prosthesis positioning and alignment restoration. The objective of this study was to provide a high-level, evidence-based comparison between robotic total knee replacements and conventional methods, focusing on radiological and functional outcomes.MethodsWe searched five databases from their inception until June 1, 2022, specifically targeting randomized controlled trials (RCTs) that compared the outcomes of robotic and conventional total knee replacements. We were interested in outcomes such as knee range of motion, clinical and function knee society scores, the Western Ontario and McMaster University score (WOMAC), the Hospital of Special Surgery score, complications, and radiological alignment. This review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes guidelines. We assessed the risk of bias using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2).ResultsOur search returned seven RCTs suitable for our analysis, which included a total of 1942 knees; 974 of these knees were implanted using robotic arms while the remaining 968 utilized jig-based knee systems. Our findings indicated that robotic knees had significantly better post-operative anatomical (OR − 0.82; 95% CI, − 1.027 to − 0.58, p value < 0.00001) and mechanical restoration (OR − 0.95; 95% CI, − 1.49 to − 0.41, p value < 0.0006). While knee range of motion (OR − 2.23; 95% CI − 4.89–0.43, p value 0.1) and femoral prosthesis position (OR − 0.98; 95% CI, − 2.03–0.08, p value 0.07) also favored robotic knees, these differences did not reach statistical significance. Both clinical and functional outcomes, as well as the rate of complications, were found to be statistically similar between the groups undergoing robotic and traditional knee replacement surgeries.ConclusionThis meta-analysis indicates that robotic total knee replacements offer superior post-operative anatomical and mechanical alignment compared to conventional total knee replacements. Despite this, clinical and functional outcomes, as well as complication rates, were similar between the two. These findings should be considered in light of potential confounding factors. More randomized controlled trials with the latest robotic systems are needed to confirm any superior functional and clinical outcomes from robotic-assisted surgeries.Level of evidenceI.

Electronics-Free Soft Robotic Knee Brace for Dynamic Unloading During Gait for Knee Osteoarthritis: A Proof-of-Concept Study

Abstract We present a novel electronics-free soft robotic knee brace which employs a closed-loop fluidic regenerative (CLFR) system for dynamic unloading in unicompartmental tibiofemoral osteoarthritis (OA). The existing dynamic unloaders are bulky, large, and heavy, and have low compliance likely due to the use of an electrical control box, which is eliminated in the CLFR system. The system consists of a commercial unloading knee brace, a spring-loaded bellow inserted under the heel inside a shoe, a soft-fluidic actuator (bladder), and tubing for fluid transfer. The novelty lies in the fact that the user's body weight (self-powered) compresses the bellow to provide energy to inflate the air bladder placed at the knee. As a result, the yielded pressure unloads the undesirable forces due to knee OA during the stance phase of gait while strategically applying no forces during the swing phase. The knee bladder contact pressure/force, the system response time, and the durability were evaluated via contact pressure measurements for six systems with varying bellow volumes and either pneumatic or hydraulic configurations. All systems produced safe pressure outputs for human skin within a tested bodyweight range of 60–90 kg. Pneumatic and hydraulic systems achieved 250 ms and 400 ms pressurization response times, respectively. During cyclic loading, pneumatic and hydraulic systems demonstrated less than 1% and ∼10% pressure loss, respectively. Overall, the CLFR system created a promising electronics-free solution for dynamically unloading the knee during gait, indicating a potential new paradigm for knee braces.

Reducing Knee Hyperextension With an Exoskeleton in Children and Adolescents With Genu Recurvatum: A Feasibility Study.

Genu recurvatum, or knee hyperextension, is a complex gait pattern with a variety of etiologies, and is often connected with knee weakness, lack of motor control, and spasticity. Because of the atypical forces placed on the soft tissues, early treatment or prevention of knee hyperextension may help prevent further degradation of the knee joint. In this study, we assessed the feasibility of a knee exoskeleton to mitigate hyperextension and increase swing range of motion in five children/adolescents who presented with unilateral genu recurvatum. Over the course of three visits, each participant practiced walking with the exoskeleton, which provided torque assistance during both stance and swing based on an impedance control law. In final validation trials, the exoskeleton was effective in reducing knee hyperextension (0.2 ± 4.7° average peak knee extension without exo to 9.9 ± 10.3° with exo) and improving swing range of motion by 14.0 ± 4.5° increase on average. However, while the exoskeleton was effective in normalizing the kinematics, it did not lead to improved spatio-temporal asymmetry measures. This work showcases a promising potential application of a robotic knee exoskeleton for improving the kinematic characteristics of genu recurvatum gait.

Comparison of conventional and robotic knee arthroplasty results: A retrospective observational study.

This study aimed to determine whether a standard anesthetic protocol consisting of combined spinal epidural anesthesia (CSEA) in conjunction with controlled hypotensive anesthesia (CHA), which was used for conventional total knee arthroplasty (cTKA), could provide equally effective anesthetic conditions for robotic total knee arthroplasty (rTKA). Data were collected from the medical records of 113 patients (median age=67 years; age range=55-84) who underwent elective unilateral cTKA (n=52) or rTKA (n=61) without a tourniquet from 2021 to 2023. The primary outcome measure was the rate of patients whose anesthetic method did not provide adequate motor and sensory block during the surgery and had to be converted to general anesthesia. The secondary outcome measure was to compare perioperative variables, including pain scores, analgesic consumption, blood loss, transfusions, and complications. In 6 patients (11.5%) in group rTKA, it was required to convert CSEA to general anesthesia at 160-180 minutes due to the pain at the operative knee and/or to the movement of the operative leg during surgery compared to none / zero in group cTKA (P=.008). Motor and sensory blocks terminated earlier than the total surgery time in those patients. Mean total surgery time was significantly higher in group rTKA than in group cTKA (151.25 ± 24.51 (120-240) minutes vs. 116.72 ± 4.99 (105-125) minutes, P < .001). Total surgery times tended to decrease gradually in group rTKA after the 11th case, indicating a learning curve for surgical performance. Conversion to general anesthesia was required only in 1 patient after the 11th case compared to the previous 5 patients. Mean pain scores and rescue analgesic consumption were higher in group rTKA at postoperative 0 hour and between 0 and 4 hours (P < .05) but similar at the following time points (P > .05). Blood loss, transfusion, and complication rates were similar (P > .05). Hospital discharge times were higher in group rTKA (P < .05). Although our standard CSEA protocol failed due to the regression of motor and sensory block during surgery in 11.5% of patients in rTKA, the CSEA technique combined with controlled hypotensive anesthesia provided similar anesthetic conditions in the remaining patients in group rTKA as in group cTKA. The CSEA may be considered an effective and safe anesthetic method for rTKA if interventions are applied to extend the duration of the CSEA for this novel surgical technique. Level III, Therapeutic Study.

Development and Dynamic State Estimation for Robotic Knee–Ankle Orthosis With Shape Memory Alloy Actuators

Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.

Disparities in Access to Robotic Knee Arthroplasty: A Geospatial Analysis

BackgroundThe utilization of robotic knee arthroplasty (RKA) continues to increase across the United States. The aim of this geospatial analysis was to elucidate if RKA is distributed uniformly across the United States or if disparities exist in patient access. MethodsPublicly available provider-finding functions for 5 major manufacturers of RKA systems were used to obtain the practice locations of surgeons performing RKA along with their associated RKA system manufacturer. The average travel distance for each county to the nearest RKA surgeon was calculated and Moran’s index clustering analysis was used to find hotspots and coldspots of RKA access. A logistic regression model was used to identify the predictive odds ratios between robotic hotspots and coldspots with county-level sociodemographic variables. Of the 34,216 currently practicing orthopedic surgeons in 2022, 2,571 have access to robotic assistance for knee arthroplasty. ResultsHotspots of increased travel time were predominantly in West South Central and West North Central census regions. Hotspots were significantly more rural and consisted of predominantly White populations, with lower median income and health insurance coverage. ConclusionsThe results of the current study align with existing literature, demonstrating absolute geographic access disparities for rural and economically disadvantaged populations. Additionally, relative access disparities persist for minority populations and individuals with high comorbidity burdens residing in urban areas.

Design and Fabrication of a Lightweight and Wearable Semirigid Robotic Knee Chain Exoskeleton

Abstract With the population of people affected by lower limb disability and physical impairments continuing to grow, engineers in response have begun to develop exoskeletons designed to assist and rehabilitate those in need. While there have been great strides and advancements in the development of exoskeletons, many of them are still too cumbersome, heavy, and expensive for most people. The project described in this paper aims to design and manufacture a wearable robotic knee exoskeleton that helps solve some of the drawbacks that exoskeletons have today. The exoskeleton is designed with lightweight and durable three-dimensional (3D)-printed PETG, TPU, and PLA components combined with soft, flexible, and wearable materials to achieve improved human–robot interaction while providing support when bending and extending the knee joint. The three main assemblies designed in this project were a 3D-printed semirigid knee chain, a 3D-printed flexible shin brace, and a motor actuator assembly mounted on a carbon fiber back plate. The semirigid knee chain is actuated using a Bowden cable which allows the heavy motor to be relocated onto the user's back. solidworks topology optimization and finite element analysis (FEA) were used to reduce weight while keeping the overall strength of the chain and ensuring the safety factor of 2. The exoskeleton was observed to be able to withstand applied torques of up to 29 N·m during the walking functionality test. This exoskeleton is also designed to be integrated into a larger hip exoskeleton system.

A Unified Controller for Natural Ambulation on Stairs and Level Ground with a Powered Robotic Knee Prosthesis.

Powered lower-limb prostheses have the potential to improve amputee mobility by closely imitating the biomechanical function of the missing biological leg. To accomplish this goal, powered prostheses need controllers that can seamlessly adapt to the ambulation activity intended by the user. Most powered prosthesis control architectures address this issue by switching between specific controllers for each activity. This approach requires online classification of the intended ambulation activity. Unfortunately, any misclassification can cause the prosthesis to perform a different movement than the user expects, increasing the likelihood of falls and injuries. Therefore, classification approaches require near-perfect accuracy to be used safely in real life. In this paper, we propose a unified controller for powered knee prostheses which allows for walking, stair ascent, and stair descent without the need for explicit activity classification. Experiments with one individual with an above-knee amputation show that the proposed controller enables seamless transitions between activities. Moreover, transition between activities is possible while leading with either the sound-side or the prosthesis. A controller with these characteristics has the potential to improve amputee mobility.

Volitional EMG Control Enables Stair Climbing with a Robotic Powered Knee Prosthesis.

Existing controllers for robotic powered prostheses regulate the prosthesis speed, timing, and energy generation using predefined position or torque trajectories. This approach enables climbing stairs step-over-step. However, it does not provide amputees with direct volitional control of the robotic prosthesis, a functionality necessary to restore full mobility to the user. Here we show that proportional electromyographic (EMG) control of the prosthesis knee torque enables volitional control of a powered knee prosthesis during stair climbing. The proposed EMG controller continuously regulates knee torque based on activation of the residual hamstrings, measured using a single EMG electrode located within the socket. The EMG signal is mapped to a desired knee flexion/extension torque based on the prosthesis knee position, the residual limb position, and the interaction with the ground. As a result, the proposed EMG controller enabled an above-knee amputee to climb stairs at different speeds, while carrying additional loads, and even backwards. By enabling direct, volitional control of powered robotic knee prostheses, the proposed EMG controller has the potential to improve amputee mobility in the real world.

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury

BackgroundWalking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals’ walking ability.MethodsFifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test–retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects.ResultsThe AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable ‘maximum hip flexor torque’ to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant.ConclusionsThe novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment.Trial registrationClinicalTrials.gov Identifier: NCT02425332.

Finding a Natural Fit: A Thematic Analysis of Amputees’ Prosthesis Setting Preferences during User-Guided Auto-Tuning

Amputees’ preferences for prosthesis settings are critical not only for their psychological well-being but also for long-term adherence to device adoption and health. Although active lower-limb prostheses can provide enhanced functionality than passive devices, little is known about the mechanism of preferences for settings in active devices. Therefore, a think-aloud study was conducted on three amputees to unravel their preferences for a powered robotic knee prosthesis during user-guided auto-tuning. The inductive thematic analysis revealed that amputee patients were more likely to use their own passive device rather than the intact leg as the reference for the natural walking that they were looking for in the powered device. There were large individual differences in factors influencing naturalness. The mental optimization of preference decisions was mostly based on the noticeableness of the differences between knee profiles. The implications on future design and research in active prostheses were discussed.

Development of a Compliant Lower-Limb Rehabilitation Robot Using Underactuated Mechanism

Most existing lower-limb rehabilitation robots (LLRR) for stroke and postoperative rehabilitation are bulky and prone to misalignments between robot and human joints. These drawbacks hamper LLRR application, leading to poor arthro-kinematic compatibility. To address these challenges, this paper proposes a novel robot with portability and compliance features. The developed robot consists of an underactuated mechanism and a crus linkage, respectively corresponding to the hip and knee joints. The underactuated mechanism is a new type of remote center of motion (RCM) mechanism with two sets of contractible slider cranks that can reduce the misalignments between robot and human joints. The underactuated mechanism is then optimized using the particle swarm optimization method, and the developed robot’s kinematic analysis is presented. The proposed robot can be simplified as a two-link mechanism with the ability to easily plan its trajectory using the modified Denavit–Hartenberg method. Finally, passive exercise trials demonstrate that the mismatch angles between the human and robot knee joints are less than 2.1% of the range of motion, confirming the feasibility and effectiveness of the proposed robot.