Simona Crea

Providing Time-Discrete Gait Information by Wearable Feedback Apparatus for Lower-Limb Amputees: Usability and Functional Validation

Here we describe a novel wearable feedback apparatus for lower-limb amputees. The system is based on three modules: a pressure-sensitive insole for the measurement of the plantar pressure distribution under the prosthetic foot during gait, a computing unit for data processing and gait segmentation, and a set of vibrating elements placed on the thigh skin. The feedback strategy relies on the detection of specific gait-phase transitions of the amputated leg. Vibrating elements are activated in a time-discrete manner, simultaneously with the occurrence of the detected gait-phase transitions. Usability and effectiveness of the apparatus were successfully assessed through an experimental validation involving ten healthy volunteers.

Controlling negative and positive power at the ankle with a soft exosuit

The soft exosuit is a new approach for applying assistive forces over the wearers body through load paths configured by the textile architecture. In this paper, we present a body-worn lower-extremity soft exosuit and a new control approach that can independently control the level of assistance that is provided during negative- and positive-power periods at the ankle. The exosuit was designed to create load paths assisting ankle plantarflexion and hip flexion, and the actuation system transmits forces from the motors to the suit via Bowden cables. A load cell and two gyro sensors per leg are used to measure real-time data, and the controller performs position control of the cable on a step-by-step basis with respect to the power delivered to the wearers ankle by controlling two force parameters, the pretension and the active force. Human subjects testing results demonstrate that the controller is capable of modulating the amount of power delivered to the ankle joint. Also, significant reductions in metabolic rate (11%-15%) were observed, which indicates the potential of the proposed control approach to provide benefit to the wearer during walking.

Development of gait segmentation methods for wearable foot pressure sensors

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Development of an Experimental Set-Up for Providing Lower-Limb Amputees with an Augmenting Feedback *

We present a proprioceptive feedback system for lower-limb amputees based on vibratory stimulation applied on the thigh. The system should be integrated in the prosthesis for overcoming the missing proprioceptive information from the amputated foot sole. It acquires data from one pressure-sensitive insole, inserted in the shoe of the amputated foot, and elaborates the acquired information for the real-time detection of specific gait-phase transitions. On the basis of the recognized transition, one of the vibrators positioned on the amputees thigh is activated.

Time-discrete vibrotactile feedback contributes to improved gait symmetry in patients with lower limb amputations: Case series

Background. Reduced sensory feedback from lower leg prostheses results in harmful gait patterns and entails a significant cognitive burden because users have to visually monitor their locomotion. O ...

Functional Design of a Powered Elbow Orthosis Toward its Clinical Employment

This paper presents the design and preliminary evaluation of a novel version of the robotic elbow exoskeleton NEUROExos, designed for the in-clinic treatment of stroke survivors in acute and subacute phases. The robotic design implements a novel series elastic actuation system, a 4-degree-of-freedom (DoFs) passive mechanism for the anatomical axis alignment, and one active DoF with remote cable-driven actuation. The low-level control system allows two working modalities: a torque control and a joint position control. The high-level control system employs a finite-state machine that allows the setting and execution of these modalities during rehabilitation exercises. Preliminary pilot tests based on passive exercises, with three chronic post-stroke patients, demonstrated the effectiveness of the proposed approach in assessing joint rigidity and its usability within a rehabilitation clinic.

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

A Real-Time Lift Detection Strategy for a Hip Exoskeleton

Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect users lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance.

A novel shoulder-elbow exoskeleton with series elastic actuators

This work presents a new shoulder-elbow exoskeleton (NESM) for upper-limb neurological rehabilitation. The system has four active degrees of freedom, namely shoulder adduction/abduction, flexion/extension and intra/extra rotation and elbow flexion/extension, together with eight additional passive degrees of freedom for the alignment of the motor axes to the human joint axes, regardless the users specific anthropometry sizes. All the four active joints employ series elastic actuators: such compliant architecture makes the device both controllable in position mode and in torque mode. In order to realize a safe human-machine interface in the rehabilitation treatment, an algorithm for detecting spastic user contractions, or equivalently collisions with external objects, based on the torque measured in real-time by the device, has been preliminary tested.

The rubber foot illusion

BackgroundLower-limb amputation causes the individual a huge functional impairment due to the lack of adequate sensory perception from the missing limb. The development of an augmenting sensory feedback device able to restore some of the missing information from the amputated limb may improve embodiment, control and acceptability of the prosthesis.FindingsIn this work we transferred the Rubber Hand Illusion paradigm to the lower limb. We investigated the possibility of promoting body ownership of a fake foot, in a series of experiments fashioned after the RHI using matched or mismatched (vibrotactile) stimulation. The results, collected from 19 healthy subjects, demonstrated that it is possible to elicit the perception of possessing a rubber foot when modality-matched stimulations are provided synchronously on the biological foot and to the corresponding rubber foot areas. Results also proved that it is possible to enhance the illusion even with modality-mismatched stimulation, even though illusion was lower than in case of modality-matched stimulation.ConclusionsWe demonstrated the possibility of promoting a Rubber Foot Illusion with both matched and mismatched stimulation.