Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

慣性センサおよび力センサを用いた立ち上がり時の関節角度推定手法に関する研究

Standing-up motion from a chair is directly connected with walking and which is frequently performed every day. It is difficult for some elders because of the weakened function of muscles or motor. The training of standing-up motion and assisting the elderly with the standing-up motion from a chair is important to the elderly Quality of Life (QOL). Analysis of the posture parameters during standing up motion is useful for the physical therapists and care-giver in rehabilitation training or movement assist. The motion capture system can measure the movement of body posture in any direction precisely. However, it is difficult to use in daily life because of high cost and specific requirements for the space. And the use of motion capture system will give unpleasant feeling to users because many reflective makers are attached in the body. The purpose of this study is to develop a new estimation system, which can be used in daily life for angle estimation of extension phase during standing-up motion. This paper discusses the estimation system consist of: 1) the estimation of body joint angles and COG during extension phase; 2) the improvement of the proposed system for angle estimation. In 1), an estimation model was proposed that was able to estimate knee and ankle joint angles by combining angle and acceleration of trunk, which came from the inertial sensor attached to the chest of users during the extension phase. The estimate result of joint angle shows higher accuracy than previous research. In 2), in order to expand the use of proposed system and improve the estimation accuracy of proposed system, we estimated the initial angle of knee and ankle by combining foot pressure which measured by a force sensor plate before standing-up motion. The estimation model of initial lower limb angle shows high accuracy. It can be used for angle estimation of extension phase even though the initial knee and ankle joint angle were unknown.九州工業大学博士学位論文 学位記番号：生工博甲第317号 学位授与年月日：平成30年3月23日1 Introduction|2 Previous Researches|3 Angle Estimation of Extension Phase|4 Estimation of Initial Lower Limb Angle|5 Conclusion and Future Work九州工業大学平成29年

Biomechanical Assessment of Ertl and Burgess Transtibial Amputation Techniques

In this dissertation, a model was developed to predict the inertial properties of the shank and foot of persons with TTA and functional differences between Ertl and Burgess amputees during curb negotiation and the sit-to-stand tasks were evaluated. The developed inertial model was able to predict the shank and foot segment mass, COM location, and MOI more accurately than using the intact limb inertial properties. Used as inputs into inverse dynamics equations, the general model predictions produced joint moments which were also similar to the subject-specific measures. Thus, this model is a better predictor than the current method of using the intact limb inertial measures for the amputated limb. The second and third studies showed differences between the Ertl and Burgess amputated limbs in functional ability. During curb negotiation the Ertl amputated limb produced net limb work (sum of ankle, knee, and hip work) similar to that of the intact limbs of both groups on the curb step. This net limb work was produced by the hip early in stance phase as a compensatory mechanism to help propel the body forward. During the sit-to-stand task, the Ertl group was able to perform the task more quickly than the Burgess group. The faster performance time was due in part to larger ground reaction forces in the Ertl amputated limb compared to the Burgess amputated limb. This suggested the Ertl limb was able to bear higher loads overall during this task. While no other differences were found between the amputated limbs, the Ertl intact limb showed unexpected differences. Where the Burgess limbs and Ertl amputated limb adopted a hip strategy to complete the task, the Ertl intact limb adopted a knee strategy. This knee strategy is more similar to the way non-amputees complete the task. Both study 2 and 3 highlighted functional advantages of the Ertl procedure over the Burgess procedure for these tasks and is, to our knowledge, the first study of its kind. Based on these outcomes, it appears that the Ertl procedure does lead to better functional performance during prosthesis use, and further consideration should be given to using this procedure at the time of amputation. Future work needs to continue to focus on functional performance in both groups and begin to contrast the outcomes with post-operative risks following the amputation to better inform patients and clinicians about potential advantages of either technique

Sit-to-Stand Phases Detection by Inertial Sensors

The Sit-to-Stand(STS) is defined as the transition from the sitting to standing position. It is commonly adopted in clinical practice because musculoskeletal or neurological degenerative disorders, as well as the natural process of ageing, deter-mine an increased difficulty in rising up from a seated position. This study aimed to detect the Sit To Stand phases using data from inertial sensors. Due to the high variability of this movement, and, consequently the difficulty to define events by thresholds, we used the machine learning. We collected data from 27 participants (13 females,24.37\ub13.32 years old). They wore 10 Inertial Sensors placed on: trunk,back(L4-L5),left and right thigh, tibia, and ankles. The par-ticipants were asked to stand from an height adjustable chair for 10 times. The STS exercises were recorded separately. The starting and ending points of each phase were identified by key events. The pre-processing included phases splitting in epochs. The features extracted were: mean, standard deviation, RMS, Max and min, COV and first derivative. The features were on the epochs for each sensor. To identify the most fitting classifier, two classifier algorithms,K-nearest Neighbours( KNN) and Support Vector Machine (SVM) were trained. From the data recorded, four dataset were created varying the epochs duration, the number of sensors. The validation model used to train the classifier. As validation model, we compared the results of classifiers trained using Kfold and Leave One Subject out (LOSO) models. The classifier performances were evaluated by confusion matrices and the F1 scores. The classifiers trained using LOSO technique as validation model showed higher values of predictive accuracy than the ones trained using Kfold. The predictive accuracy of KNN and SVM were reported below: \u2022 KFold \u2013 mean of overall predictive accuracy KNN: 0.75; F1 score: REST 0.86, TRUNK LEANING 0.35,STANDING 0.60,BALANCE 0.54, SITTING 0.55 \u2013 mean of overall predictive accuracy SVM: 0.75; F1 score: REST 0.89, TRUNK LEANING 0.48,STANDING 0.48,BALANCE 0.59, SITTING 0.62 \u2022 LOSO \u2013 mean of overall predictive accuracy KNN: 0.93; F1 score: REST 0.96, TRUNK LEANING 0.79,STANDING 0.89,BALANCE 0.95, SITTING 0.88 \u2013 mean of overall predictive accuracy SVM: 0.95; F1 score phases: REST 0.98, TRUNK LEANING 0.86,STANDING 0.91,BALANCE 0.98, SIT-TING 0.9

Novel instrumented frame for standing exercising of users with complete spinal cord injuries

This paper describes a Functional Electrical Stimulation (FES) standing system for rehabilitation of bone mineral density (BMD) in people with Spinal Cord Injury (SCI). BMD recovery offers an increased quality of life for people with SCI by reducing their risk of fractures. The standing system developed comprises an instrumented frame equipped with force plates and load cells, a motion capture system, and a purpose built 16-channel FES unit. This system can simultaneously record and process a wide range of biomechanical data to produce muscle stimulation which enables users with SCI to safely stand and exercise. An exergame provides visual feedback to the user to assist with upper-body posture control during exercising. To validate the system an alternate weight-shift exercise was used; 3 participants with complete SCI exercised in the system for 1 hour twice-weekly for 6 months. We observed ground reaction forces over 70% of the full body-weight distributed to the supporting leg at each exercising cycle. Exercise performance improved for each participant by an increase of 13.88 percentage points of body-weight in the loading of the supporting leg during the six-month period. Importantly, the observed ground reaction forces are of higher magnitude than other studies which reported positive effects on BMD. This novel instrumentation aims to investigate weight bearing standing therapies aimed at determining the biomechanics of lower limb joint force actions and postural kinematics

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Probabilistic Musculoskeletal Simulation Methods to Address Intersegmental Dependencies of the Knee, Hip, and Spine

Orthropaedic clinical practice in the area of the knee, hip, and spine has benefited from the concept of regional interdependence, where interventions applied to one region can influence the outcome and function of other regions of the body that may be seemingly unrelated to the applied intervention. An understanding of the biomechanical mechanisms that describe clinical practice involving knee, hip, and spine regional interdependence can improve treatment of a wide range of pathological conditions. Improvement in this area can be particularly impactful on the outcomes of patients with total joint replacement, where pathology and compensatory strategies develop during multi-joint interactions. Additionally, probabilistic methods are well suited to address knee, hip, and spine regional interdependence by using input distributions to quantify the impact of variability on the range of possible output variables. Outputs from probabilistic methods include variable interaction effects and provides sensitivity information, resulting in a more comprehensive evaluation of a system The main objectives of the work presented in this dissertation were to further our understanding of the interdependencies of the knee, hip, and spine with probabilistic musculoskeletal modeling. These objectives were achieved by developing a probabilistic plugin for use in OpenSim and performing investigations of the regional interdependence of the knee, hip, and spine involving patients with total joint replacement. An initial study identified how uncertainty in musculoskeletal simulation inputs can propagate through the stages of analysis and impact interpretation of outputs from a simulation of gait. Second, improvements to current modeling methodology for patients with total hip arthroplasty were made through the implementation of patient-specific strength scaling and input uncertainty assessment. The third study then applied these methods in an investigation of knee, hip, and spine regional interdependence in rehabilitation of patients with total hip arthroplasty to quantify the influence of simulated strengthening of hip musculature on the dynamic and mechanical interdependencies of the knee, hip and spine. A final study demonstrated how population-based musculoskeletal modeling can further impact the study of knee, hip, and spine regional interdependence by presenting the feasibility study of performing population-based musculoskeletal modeling. These studies include several novel methods for investigating the regional interdependencies of the knee, hip, and spine that have been used to translate outputs from musculoskeletal simulations into rehabilitation practice