Thigh Angles Research Articles

A NEW TEMPORARY TRAINING PROSTHESIS FOR PEOPLE WITH TRANSTIBIAL AMPUTATION: A TECHNICAL NOTE

BACKGROUND: While waiting to receive a prosthesis, individuals with amputations could benefit from using a temporary training prosthesis to expedite the rehabilitation process and prepare them for subsequent walking with their prosthesis. OBJECTIVES: To design and build a temporary training prosthesis for people with a transtibial amputation. METHODOLOGY: Various temporary training prostheses were designed and simulated using SolidWorks software, followed by fabricating and testing multiple prototypes. Initial tests were conducted on five able bodied subjects without amputation to evaluate comfort, ensure the prototype functioned as intended, and to refine the design. The final prototype design had no weight-bearing on the residual limb end and required the person to wear a shrinker or silicone liner. FINDINGS: SolidWorks simulations showed that the device could tolerate up to 200 kg load. Subjective feedback indicated that body weight is primarily supported by the thigh section, while partially utilizing the patellar tendon and tibial flares. The thigh section can be shifted 5 cm up or down and 2.5 cm to the front or back from the knee joint center (to enhance knee stability or function). Additionally, the thigh angle can be adjusted to 0, 5, 10, or 15 degrees to accommodate hip flexion contracture. The shank section width is adjustable and can be shifted up or down based on the residual limb shape. All five able-bodied participants successfully walked with the non-amputee version of the temporary prosthesis prototype and the device withstood walking, sitting, and standing loads. CONCLUSION: An adjustable temporary training prosthesis was successfully designed, and pilot tested by five able-bodied individuals. Future testing will involve five experienced prosthetic users before conducting trials with individuals with a new transtibial amputation. Layman's Abstract A novel temporary training prosthesis was developed for individuals with new transtibial amputations while they await the healing of their residual limb and the receipt of a prosthesis. Utilizing SolidWorks computer-aided design software, various designs were explored, and rigorous testing was conducted to ensure durability and functionality. Adjustable features were incorporated to accommodate different body shapes and sizes. Initial testing on five people without amputations confirmed device effectiveness in supporting walking, sitting, and standing. Next, performance will be assessed on five experienced prosthetic users before proceeding to trials with individuals with a new transtibial amputation. This temporary prosthesis aims to expedite rehabilitation, enabling people to regain mobility and independence more efficiently. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43034/32865 How To Cite: Gholizadeh H, Baddour N, Dudek N, Lemaire E.D. A new temporary training prosthesis for people with transtibial amputation: A technical note. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 1, No.2. https://doi.org/10.33137/cpoj.v7i1.43034 Corresponding Author: Hossein Gholizadeh, PhD, CP (Canada)Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada.E-Mails: hgholizadeh@uottawa.ca, gholizadeh87@yahoo.comORCID ID: https://orcid.org/0000-0001-5847-7985

Dynamic Modelling and Optimal Sliding Mode Control of the Wearable Rehabilitative Bipedal Cable Robot with 7 Degrees of Freedom

Although robot-assisted physiotherapy has gained increasing attention in recent years, the use of wearable rehabilitation robots for lower limbs has shown reduced efficiency due to additional equipment and motors located at the center of the joint, increasing complexity and load on disabled patients. This paper proposes a novel rehabilitation approach by eliminating motors and equipment from the center of joints and placing them on a fixed platform using cable-based power transmission. A proposed model of a 14 cable-driven bipedal robot with 7 degrees of freedom has been used to model a lower limb rehabilitation robot corresponding to it. The dynamic equations of the robot are derived using the Euler-Lagrange method. The sliding mode control technique is utilized to offer accurate control for tracking desired trajectories, ensuring smoothness despite disturbances, and reducing tracking errors. This approach is employed to help prevent patients from falling and support them in maintaining balance during rehabilitative exercises. To ensure that cables exert positive tension, the sliding mode controller was combined with quadratic programming optimization, minimizing path error while constraining the controller input torque to be non-negative. The performance of the proposed controller was assessed by considering several control gains resulting in K = 10 identified as the most effective one. The feasibility of this approach to rehabilitation is demonstrated by the numerical results in MATLAB simulation, which show that the RMSE amount of the right and left hip and thigh angles are 0.29, 0.37, 0.31, and 0.44, respectively which verified an improved rehabilitation process. Also, the correlation coefficient between the Adams and MATLAB simulation results for motor torque was found to be 0.98, indicating a high degree of correlation between the two simulation results.

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales.MethodsThis approach is mainly based on movement quantization through analysis of variance in patients’ skeletons detected in outpatient videos. The patients’ skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations.ResultsThe results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor “thigh angle”, with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%).ConclusionsThe proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Longitudinal study of three-dimensional pelvic behaviour in maximal sprint running

The kinematics and kinetics of pelvis are associated with sprint performance. In this study, we aimed to investigate the longitudinal changes in the kinematics and kinetics of the pelvis in response to increasing sprinting velocity. Nine male sprinters performed 60 m regular sprints starting from a crouching start position, once a year. A three-dimensional motion analysis was performed to longitudinally investigate the changes in the pelvic movements and force exertion characteristics during sprinting. Sprinting velocity was significantly higher in the post-test than the pre-test. Step frequency was significantly higher in the post-test than the pre-test. The pelvic anterior/posterior tilt angle at stance leg touch-down, stance leg toe-off, and free leg touch-down were significantly smaller in the post-test than the pre-test. The thigh angle of the stance leg at stance leg toe-off and free leg touch-down were significantly smaller in the post-test than in the pre-test. The integrated contributory component of the lumbosacral joint torsion angular impulse during the stance phase was significantly greater in the post-test than the pre-test. This study provided new insights into the longitudinal evaluation of sprint performance in the transverse plane, focusing on pelvic movement and force exertion characteristics.

The Foot-Gotten Variable – Modified Assessment for Measuring the Rotational Profile

The assessment of the rotational profile in the lower limb involves the evaluation of both the tibia and femur. Common methods for assessing tibial torsion include the Transmalleolar Axis (TMA) and Thigh Foot Angle (TFA). While TMA and TFA are highly reliable, their validity is limited, often overlooking a crucial variable – the foot. External rotated TFA is typically linked to a pronated foot, while a supinated foot is associated with internal rotated TFA. This association arises from foot alignment, rendering the measurement not truly reflective of tibial torsion. We propose measuring tibial torsion in subtalar neutral and in a simulated weight-bearing position to address the impact of foot alignment on TFA measurement.Conclusion - This innovative approach to TFA measurement has the potential to enhance diagnostic accuracy in clinical settings, thereby improving clinical decision making regarding treatment choices. Short Title. Modified Assessment for Foot Thigh Angle

Implementing an accelerometer-based pelvis segment for low back kinetic analyses during dynamic movement tasks

An accelerometer-based pelvis has been employed to study segment and joint kinematics during scenarios involving close human-object interface and/or line-of-sight obstructions. However, its accuracy for examining low back kinetic outcomes is unknown. This study compared reaction moments and contact forces of the L5S1 joint calculated with an accelerometer-based and optically tracked pelvis segment. An approach to correct the global pelvis position as a function of thigh angle was developed. One participant performed four dynamic tasks: forward bend, squat, sit-to-stand-to-sit, and forward lunge. A standard bottom-up inverse dynamics approach was used and the root mean square error (RMSE) and coefficient of determination (R2) were calculated to examine kinetic differences between the optical and accelerometer approaches. The RMSE observed for L5S1 reaction flexion-extension moments ranged from 1.32 Nm to 2.20 Nm (R2 ≥ 0.98). The RMSE for net shear and compression reaction forces ranged from 2.13 to 10.45 N and 0.63 – 4.96 N, respectively. Similarly, the RMSE for L5S1 joint contact shear and compression ranged from 13.45 N to 19.51 N (R2 ≥ 0.85) and 31.18 N – 55.97 N (R2 ≥ 0.97), respectively. In conclusion, the accelerometer-based pelvis together with the approach to correct the global pelvis position is a feasible approach for computing low back kinetics with a single equivalent muscle model. The observed error in joint contact forces represents less than 5 % of the NIOSH recommended action limits and is unlikely to alter the interpretation of low back injury risk.

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Acute effects of sprint training for hamstrings injury prevention on male college soccer players

In soccer, the hamstring is one of the most common injury portions. Among the various interventions, it can be assumed that modification of sprinting movement is directly helpful in hamstring injuries. The acute effects of sprinting interventions would be useful for pre-match interventions if they were immediate. The present study aimed to clarify the acute effects of sprint training for hamstring injury prevention on a collegiate soccer player. A total of twenty–seven male collegiate soccer players participated in the present study. Participants performed a 30 m sprint test as a pre-test after warming up. Subsequently, an hour-long sprint training session, targeting the modification of movements associated with hamstring injuries, was conducted, followed by a post-test involving a 30 m sprint. From these trials, 30 m time and kinematic variables associated with a hamstring injury; trunk angle, thigh angle and shank angle, were computed. The results revealed a significant positive modification in the trunk angle between the pre-test and post-test sessions. However, no marked differences were observed in 30 m time and other kinematic data. Therefore, sprinting modification was shown to have an acute effect on improving trunk angle without affecting the running speed.

Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs

BackgroundProsthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets.MethodsThe conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients.ResultsThe trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load.ConclusionsThis study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

Evaluation of maximum thigh angular acceleration during the swing phase of steady-speed running

ABSTRACT The hip joint and surrounding musculature must generate and withstand torque during the swing phase of running. Prior research has demonstrated that sagittal plane hip torque increases with speed, indicating that thigh angular acceleration likely increases in a similar manner and may be an important gait parameter. In this investigation, we modelled thigh angle vs. time data with a sine wave function, requiring inputs of thigh angular amplitude and stride frequency. This enabled a simple formula to model maximum thigh angular acceleration (αmax , rad/s2) during the swing phase of steady-speed running. A total of 40 participants (20 male, 20 female) completed submaximal and maximal 40 m running trials (n = 154 trials, speed range: 3.1–10.0 m/s), with kinematic data collected from 31–39 m. Thigh angle vs. time curves were well fit by a sine wave function (mean R2 > 0.94 across all trials) and modelled αmax was highly correlated with top speed (R2 = 0.81, p < 0.001). We conclude that thigh angular acceleration is an important parameter when examining running performance across a range of speeds and the simple method introduced here to model αmax may have practical utility for future examinations into high-speed running mechanics.

Using embedded prosthesis sensors for clinical gait analyses in people with lower limb amputation: A feasibility study

BackgroundBiomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint. MethodsTen participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors. FindingsThe average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh). InterpretationThe findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab.

Comparison of theoretical and experimental models for knee flexion/extension using a cable-driven robot

AbstractTo support and facilitate the rehabilitation of patients with physical limitations and aid the therapist, several robotic structures are being studied. Among the structures, the cable-driven robots stand out. The cable-driven robots are structures actuated by cables and have the advantages of being flexible and reconfigurable for each patient. The objective of this paper is to develop a theoretical model for knee flexion/extension force and moment using a cable-driven robot. The proposed model is necessary for elaborating a referential to which diagnosis can be made and the improvement of the patient evaluated. The presented theoretical model was validated through experiments with twelve sedentary and healthy volunteers. The first procedure tested ten subjects in three thigh angles for knee flexion motion; the second procedure tested two subjects in flexion and extension for the same thigh angle. The results show the validity of the model for 88.58% of the tests in an ANOVA analysis with a 99% confidence interval. The similarity of data for different gender, ages, and intrinsic factors was noted, implying that the model is representative and independent of the subject’s individuality. Differences between flexion and extension values were observed, which need to be studied in the future.

A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs.

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.

Multi-Joint Leg Moment Estimation During Walking Using Thigh or Shank Angles.

To reinstate human-like locomotion by using robotic prosthetics, orthotics or exoskeletons, a main challenge is how to coordinate the motion of these devices with that of the biological limbs. One approach to overcome this challenge is to identify firstly the relationships that exist between the kinematics and kinetics of the lower extremity joints and limbs. In this work we aimed to continuously estimate sagittal plane ankle, knee and hip moments using shank or thigh angles. For this purpose, neural network and wavelets were used in a nonlinear auto-regressive model with exogenous inputs. This approach circumvented the need for switching rules or intermediate parameters. To assess the performance of the estimator, four case studies were developed. First, thigh angles (inputs) were used to estimate hip moments (outputs). Second, thigh angles were used to estimate knee moments. Third, ankle moments were estimated using thigh angles, and in the fourth case study, ankle moments were estimated using shank angles. Three different databases involving 106 subjects at different walking speeds were used to evaluate estimation quality. The testing procedure involved both inter-subject and intra-subject evaluations. The best estimation performance was observed when ankle moments were estimated from shank angles. The weakest estimation performance was observed when knee moments were estimated using thigh angles at 0.5 m/s. For this case, the estimation quality was much better at 1.5 m/s. Average RMS errors were 0.13- 0.15, 0.10- 0.13, and 0.09- 0.12 [Nm/kg] for hip, knee and ankle moments, respectively. Average mean absolute errors MAEs were 0.10- 0.11, 0.07- 0.10, and 0.06- 0.08 [Nm/kg] for hip, knee and ankle moments, respectively. Average correlation coefficients were 0.90- 0.98 and 0.98- 0.99 for hip and ankle moment estimations. The value for knee was comparable only at high speed (0.96 for 1.5 m/s), while it was less accurate at slow speed (0.71 for 0.5 m/s). In general, for all of the joints, the estimation accuracy was comparable with that of other studies, although one source of input was employed (either shank or thigh angle).

Neuromotor changes in participants with a concussion history can be detected with a custom smartphone app.

Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture.

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Sit, Stand, and Walk with Minimal Tuning.

Although the average healthy adult transitions from sit to stand over 60 times per day, most research on powered prosthesis control has only focused on walking. In this paper, we present a data-driven controller that enables sitting, standing, and walking with minimal tuning. Our controller comprises two high level modes of sit/stand and walking, and we develop heuristic biomechanical rules to control transitions. We use a phase variable based on the user's thigh angle to parameterize both walking and sit/stand motions, and use variable impedance control during ground contact and position control during swing. We extend previous work on data-driven optimization of continuous impedance parameter functions to design the sit/stand control mode using able-bodied data. Experiments with a powered knee-ankle prosthesis used by a participant with above-knee amputation demonstrate promise in clinical outcomes, as well as trade-offs between our minimal-tuning approach and accommodation of user preferences. Specifically, our controller enabled the participant to complete the sit/stand task 20% faster and reduced average asymmetry by half compared to his everyday passive prosthesis. The controller also facilitated a timed up and go test involving sitting, standing, walking, and turning, with only a mild (10%) decrease in speed compared to the everyday prosthesis. Our sit/stand/walk controller enables multiple activities of daily life with minimal tuning and mode switching.

The biomechanical benefits of active sitting

This cross-sectional study examined the biomechanical effects of two active chairs (AC1: had the feature to pedal and slide forward on the seat pan; AC2: a multiaxial motion seat pan) compared to a traditional office chair and standing workstation. Twenty-four healthy participants worked at each of the workstations for 60-min. The following equipment was used: Motion Capture, Electromyography, Ratings of Perceived Discomfort Questionnaire, and Exit Survey. The active protocol had positive effects on the body, including increased neuromuscular activity in the gastrocnemius, increased overall movement, and a more open trunk–thigh angle. Greater discomfort in the buttocks due to the lack of seat pan contour was reported for the AC1 which identified a need for a design modification. While standing, participants’ shoulders were less flexed than when sitting in any of the three seats, however, greater discomfort was reported in the lower legs after 1 h of computer work. Practitioner summary: A comparison of four different workstations was conducted to further understand the use of active workstations. Active sitting was found to have positive effects on the body, such as allowing sitters to increase movement while sitting without the high activation of muscular activity. Standing can also provide a positive break from sitting.

Designing a 3D virtual test platform for evaluating prosthetic knee joint performance during the walking cycle

AbstractThis article introduced a three-dimension CAD model of a prosthesis testing platform using SolidWorks software to conduct a kinematic and dynamic analysis of the transfemoral prosthesis of the virtual model. Concurrently, the event-based motion simulation (EBMS) procedure was carried out on the CAD model. The concept of the operational strategy of the test platform was clarified through the machine’s real-life experience before being constructedin vitro. The platform model is capable of reproducing two active movements to simulate the locomotion of the thigh angle and hip vertical displacement for assessing the artificial knee angle motion during the gait cycle. These motions were controlled by two rotary forces (motors) that are utilized to implement control actions in EBMS. The prosthetic knee joint was built with a single axis that performs flexion and extension via the axial force of the spring. The simulation results of the thigh angle motion ranged from20°2{0}^{^\circ }to−15°-1{5}^{^\circ }, while the maximum flexion of the prosthetic knee joint was (46°4{6}^{^\circ }). The mean absolute error was (2.727°2.72{7}^{^\circ }) and (8.338°8.33{8}^{^\circ }) for the thigh and knee joints, respectively. In conclusion, the findings can be utilized to facilitate the design and development of prostheses.

A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this letter, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.

Recognition and Analysis of an Age-Friendly Intelligent Sofa Design Based on Skeletal Key-Points.

The aging population has a higher level of consumption willingness, higher quality of life demands, and more diversified spiritual pursuits. In recent years, age-friendly industries have entered a ‘blue ocean of development’; intelligent recreation and age-friendly industries have become new growth points in the double-cycle situation; however, the current generalized design of the market is not enough to meet the needs of its elderly users. Through an extensive research and demand analysis focused on the highly-frequent use of sofas by elderly individuals, an AHP analysis showed that smart sofa design guidelines are among the most important functional indicators; user ‘pain points’ focused on low seat surfaces caused by the difficulty in getting up. To further refine this action behavior, a Kinect-based experimental device was used to capture the behavior of the elderly user during the sit-to-stand transition. The experimental data were collected from 25 key skeletal points in the human body and further investigated by converting the skeletal points into 12 key joint angles to refine the joint transition threshold risk during the sit-to-stand transition for the elderly user. The test results show that the most important joint angle affecting the sit-to-stand transition process is the trunk thigh angle was 5-2, with an important value of 0.122. The two-dimensional body data of the elderly user was mapped to the joint angles under the three-dimensional activity threshold to build a comfort model of the elderly user’s sofa, providing a theoretical basis for the design parameters of the aging sofa. In response to the research results, an intelligent age-friendly sofa with three forms was designed and prototyped independently, meeting the universal size of elderly users in terms of dimensional parameters, and iterating leisure and assisted standing transformation in terms of function (to reduce the risk levels of the sitting and standing transformations of elderly users).