Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living

The development of a portable assistive device to aid patients affected by neuromuscular disorders has been the ultimategoal of assistive robots since the late 1960s. Despite significant advances in recent decades, traditional rigid exoskeletonsare constrained by limited portability, safety, ergonomics, autonomy and, most of all, cost. In this study, we present thedesign and control of a soft, textile-based exosuit for assisting elbow flexion/extension and hand open/close. We describea model-based design, characterisation and testing of two independent actuator modules for the elbow and hand,respectively. Both actuators drive a set of artificial tendons, routed through the exosuit along specific load paths, thatapply torques to the human joints by means of anchor points. Key features in our design are under-actuation and the useof electromagnetic clutches to unload the motors during static posture. These two aspects, along with the use of 3Dprinted components and off-the-shelf fabric materials, contribute to cut down the power requirements, mass and overallcost of the system, making it a more likely candidate for daily use and enlarging its target population. Low-level control isaccomplished by a computationally efficient machine learning algorithm that derives the system’s model from sensorydata, ensuring high tracking accuracy despite the uncertainties deriving from its soft architecture. The resulting system isa low-profile, low-cost and wearable exosuit designed to intuitively assist the wearer in activities of daily living

EMG-based learning approach for estimating wrist motion

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R2 index of 63:6% is obtained for generalization across different arm positions and wrist joint angles

Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines

10.1186/s12984-016-0183-0Journal of NeuroEngineering and Rehabilitation13118

Decoding human motion intention using myoelectric signals for assistive technologies

Diseases or trauma affecting either the sensory or motor functions in humans leads to movement impairment. They severely affect the independence of the user and the ability to perform activities of daily living. Assistive technologies aim to function in parallel to the affected human body and provide assistance. However, it is important for the robots to understand the user’s motion intention so as to provide assistance in the desired movement; the intention detection is especially challenging in the case of upper-limb motions which are primarily involved in performing dexterous manipulation tasks. Myoelectric signals are capable of providing relevant information about the intent of motion and the extent of effort applied by a person. As such, it is a practically viable solution to utilize electromyographic (EMG) signals to build intuitive human-machine interfaces for applications in prosthetics, orthotics, tele-manipulation and functional electrical stimulation. However, there are quite a lot of challenges with regards to reliably translating the human intention for functional use and efficient control of a multifunctional device. The primary reason is that the EMG signals are time-varying and noisy. Moreover, there is a complex non-linear relationship between the numerous muscles and the corresponding output forces. The aim of this thesis is in providing improvements and solutions to some of the limitations in decoding myoelectric signals, and the work is centered around four themes with focus on the upper limb motions. The first goal is to identify strategies for improving the reliability of myoelectric-based motion decoding. We explored the use of extreme learning machines and quantified its performance for online decoding and evaluating the differences in accuracy while using both muscle synergy and time-domain based features. Secondly, our focus is on building efficient algorithms by using dimensionality reduction techniques to simplify the control complexity; we explored the use of non-negative matrix factorization and linear discriminant analysis for improving and enhancing the decoding capability of EMG-based control interfaces. The third objective is to incorporate simultaneous decoding capability, thereby enable dexterous control of the device by the user. We developed and evaluated the decoding performance of an algorithm capable of classifying both simple and compound movements, by recording only the EMG activity associated to simple movements. The final goal is to implement a user-modulated position and stiffness control of an exoskeleton device, to transfer the impedance characteristics of the human to the device, thereby enabling better transparency and safety in applications involving human-machine interactions. We evaluated the efficacy of simple models to identify the stiffness characteristics from the user's EMG signals, in a trajectory tracking task.Doctor of Philosoph

An LDA-Based Approach for Real-Time Simultaneous Classification of Movements Using Surface Electromyography

Myoelectric-based decoding strategies offer significant advantages in the areas of human-machine interactions because they are intuitive and require less cognitive effort from the users. However, a general drawback in using machine learning techniques for classification is that the decoder is limited to predicting only one movement at any instant and hence restricted to performing the motion in a sequential manner, whereas human motor control strategy involves simultaneous actuation of multiple degrees of freedom (DOFs) and is considered to be a natural and efficient way of performing tasks. Simultaneous decoding in the context of myoelectric-based movement control is a challenge that is being addressed recently and is increasingly popular. In this paper, we propose a novel classification strategy capable of decoding both the individual and combined movements, by collecting data from only the individual motions. Additionally, we exploit low-dimensional representation of the myoelectric signals using a supervised decomposition algorithm called linear discriminant analysis, to simplify the complexity of control and reduce computational cost. The performance of the decoding algorithm is tested in an online context for the two DOFs task comprising the hand and wrist movements. Results indicate an overall classification accuracy of 88.02% for both the individual and combined motions

Leveraged buyout : Investeringskriterier

Sammanfattning Utvecklingen och utbredningen av Leveraged buyouts förvärv har ökat i Sverige de senaste 5-15 åren, detta kan vara ett resultat av ett mer flöde på kapital och intresse av att investera. Dessutom har förmodligen den allt mer mogna private equity marknad tillsammans med det ökande behovet av effektivisering av bolagen bidragit till att allt fler förvärv sker med belånat kapital. Vi har i denna kvalitativa uppsats intervjuat tre private equity företag med syfte att försöka klargöra vilka investeringskriterier som ligger till grund för en leveraged buyout-transaktion, samt vilka branscher som lockar dessa private equity företagen. I slutsatsen har det framkommit att PE-företagen gör investeringar i mogna företag utan några preferenser på specifika branscher. Vidare har vi funnit att de två viktigaste faktorer som PE-företagen tar i beaktning vid investeringar är stabila kassaflöden och kompetent företagsledning i målbolagen

Hierarchical Cascade Controller for Assistance Modulation in a Soft Wearable Arm Exoskeleton

In recent years soft wearable exoskeletons, commonly referred to as exosuits, have been widely exploited in human assistance. Hence, a shared approach for a systematic and exhaustive control architecture is extremely important. Most of the exosuits developed so far employ a bowden cable transmission to conveniently place the actuator away from the end-effector. While having many advantages this actuation strategy presents some intrinsic limitations caused by the presence of nonlinearities, such as friction and backlash of the cables, which make it difficult to predict and control the dynamics between the device and the user. In this letter, we propose a novel hierarchical control paradigm for a cable-driven upper limb exosuits that aims at evaluating and consequently deliver the appropriate assistive torque to the user's elbow joint. The proposed control method comprises three main layers: an active impedance control which estimates the user's arm motion intention and guarantees an intuitive response of the suit to the wearer's motion; a mid-level controller which compensates for the backlash in the transmission and converts the reference arm motion to the desired position of the actuator; a low-level controller which is responsible for driving the actuation stage by compensating for the nonlinear dynamics occurring in the bowden cable to provide the desired assistive torque at the joint. Tests on healthy subjects show that wearing the exosuit reduces by 48.3% the muscular effort required to lift 1 kg and that the controller is able to modulate its level of assistance to the wearer's motor ability

Design and embedded control of a soft elbow exosuit

The use of soft materials to transmit power to the human body has numerous advantages, amongst which safety and kinematic transparency stand out. In previous work we showed that a tethered fabric-based exosuit for the elbow joint, driven by an electric motor through a Bowden cable transmission, reduces the muscular effort associated with flexion movements by working in parallel with its wearer's muscles. We herein propose a refined design of the suit and present an untethered control architecture for gravity compensation and motion-intention detection. The architecture comprises four interconnected modules for power management, low-level motor control and high-level signal processing and data streaming. The controller uses a silicone stretch sensor and a miniature load cell, integrated in the fabric frame, to estimate and minimise the torque that its user needs to exert to perform a movement. We show that the device relieves its wearer from an average of 77% of the total moment required to sustain and move a light weight, with a consequent average reduction in muscular effort of 64.5%

Design and preliminary testing of a soft exosuit for assisting elbow movements and hand grasping

Most of the currently available exoskeletons for upper limbs are constrained by limited portability, ergonomics, weight and, energy-wise, autonomy. Moreover, their high cost makes them available only for the most affluent users, ruling out the majority of the population in need. By replacing rigid aluminum links and transmissions with fabrics and bowden cables, we can both cut down the cost of the assistive device and design it to be portable, comfortable and lightweight. We present the design and a preliminary testing of a soft exosuit for assisting elbow flexion/extension and hand open/close. Our system comprises two proximally located tendon-driving actuators, and two textile-based frames that route the tendons and transmit forces to the human joints, namely an elbow sleeve and a glove. A preliminary test on a healthy subject is presented with an adaptive controller that achieves good tracking accuracy despite of the system’s non-linear and time-varying dynamics