Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb’s weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

Establishment of a New Equation for Ultrasonographic Estimated Foetal Weight in Chongqing: a Prospective Study

Background: Variations in foetal growth between populations should not be ignored, and a single universal standard is not appropriate for everyone. Therefore, it is necessary to develop a new ultrasound estimation equation that adapts better to regional population characteristics. The purpose of this study was to create a new equation for ultrasound estimation of foetal weight according to the local population in Chongqing and compare it with representative equations. Methods: This prospective study included data on pregnant women who gave birth to a child at full term in our hospital from December 2016 to November 2019. Foetal ultrasound parameters included biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur diaphysis length (FDL). The foetal weight compensation model was established by using the second-order linear regression model, and then, the foetal weight equation was established by utilizing the multiple reverse elimination regression technique. Last, the absolute error and relative error were used to compare the accuracy of the equations established in this study with representative equations. Results: Through the foetal weight compensation equation, the new equation suitable for Chongqing foetuses was successfully established with the variables of BPD, HC, AC, and FDL. The following foetal weight prediction equation was established in this study: Log 10 (EFW)=3.002741+0.00005944*(BPD^2)+0.00000222*(HC^2)-0.000002078*(AC^2)+0.00004262*(FDL^2)-0.008753*BPD-0.000884*HC+0.003206*AC-0.002894*FDL (BPD: mm; HC: mm; AC: mm; FDL: mm). In the sets established by the 1925 data, the mean absolute error and standard deviation of the estimation error of the new equation were 178.9 g and 140.3 g respectively. In the validation sets established with 300 data points, the mean absolute error and standard deviation of the new equation were 173.08 g and 128.59 g respectively. Compared with representative equations, the mean absolute error and the standard deviation of the new equation were the lowest. The equation established in this study better predicted foetal weight(P<.001). Conclusions: According to the local population characteristics of Chongqing, this study created a foetal weight estimation equation that is more accurate and suitable. This equation is clinically valuable for the monitoring and management of foetal weight.

Human Weight Compensation with a Backdrivable Upper-Limb Exoskeleton: Identification and Control

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb’s weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

A New Weight Compensation Model Considering Joint Misalignments Monitored by a Feedforward Impedance Control Method Applied To an Active Upper-Limb Exoskeleton

Background Active exoskeletons are promising devices for improving rehabilitation procedures in patients. In particular, exoskeletons implementing human limb's weight support (WS) are interesting to restore some mobility in patients with muscle weakness. Using active exoskeletons should result in accurate and generic WS but its effect on human motor control will critically depend on the position of the user within the exoskeleton and the characteristics of the control law. Methods The present study aims at improving WS of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments are respectively conducted on 29, 17 and 19 participants who performed posture maintenance and pointing movements with the forearm in the sagittal plane. The first two experiments were used to build an accurate WS control law and the third experiment was conducted to compare the effects of different WS control laws on human movement and assess their quality. During these three experiments, kinematic data and eletromyographic activity of elbow flexors and extensors were measured. Interaction forces were measured with a force/torque sensor placed between the human segment and the robot link. Results The introduction of joint misalignments in the WS model allowed to drastically reduce the model errors in terms of weight estimation. The use of a feedforward architecture based on model and errors learned during experiments, coupled to a force feedback, allowed to reduce model tracking errors in both static and dynamic conditions during vertical movements, which induce substantial variations of gravitational torques. Overall, WS did not affect the general kinematic motion parameters of the participants and decreased the activity of antigravity muscles (flexors). However, WS increased the activation of extensors because weight is usually exploited by humans to accelerate a limb downward. Conclusion A new weight compensation model considering joint misalignments was introduced and data showed their prominent role on WS accuracy and homogeneity. Three WS control laws were compared and results indicated that classical control methods were not sufficient to provide an accurate tracking of the weight model during dynamic vertical movements but that introducing simple feedforward terms learned from previous measures could significantly improve WS accuracy. Accordingly, WS reduced significantly activity in flexors in both static and dynamic conditions. Nevertheless, WS tended to increase the activity of extensors, which might be an important factor in a rehabilitation perspective. Indeed, if the present WS control law will be very helpful to allow patients accelerating the arm upward despite some muscle weakness, it may have an opposite effect when accelerating the arm downward. A partial WS controller could thus be more appropriate in rehabilitation applications.

Human-in-the-Loop Weight Compensation and Mass Estimation in Upper Limb Wearable Robots Towards Muscles’ Effort Minimization

In this paper: (1) We present a novel human-in-the-loop adaptation method for whole arm muscles’ effort minimization by means of weight compensation in the face of an object with unknown mass. (2) This adaptation rule can also be used as a cognitive model for the identification of mass value. (3) This adaptation rule utilizes the EMG signal of only four muscles in the upper limb to minimize the whole muscles’ effort. The method is analyzed from analytical, simulation, and experimental perspectives. We analytically discuss the stability, optimality, and convergence of the proposed method. This method’s effectiveness for whole muscles’ effort reduction is studied by simulations (OpenSim) on a generic and realistic model of the human arm, a model with 7-DOF and 50 Hill-type-muscles. In addition, the applicability of this method in practice is experimented with by a 2-DOF arm assist device for two different tasks; static (holding an object) and cyclic (reaching point-to-point) tasks. The simulations and experimental results show the presented method’s performance and applicability for weight compensation in upper limb assistive exoskeletons. In addition, the simulations in OpenSim completely support that the suggested set of mono-articular muscles are sufficient for whole muscles’ effort reduction.