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  • Periodic Reporting for period 2 - eNHANCE (eNHANCE - intention based enhancement of reaching and grasping in physically disabled people - personalized to maximize user performance)
H2020

eNHANCE Report Summary

Project ID: 644000
Funded under: H2020-EU.2.1.1.4.

Periodic Reporting for period 2 - eNHANCE (eNHANCE - intention based enhancement of reaching and grasping in physically disabled people - personalized to maximize user performance)

Reporting period: 2016-08-01 to 2017-07-31

Summary of the context and overall objectives of the project

The overall objective of the eNHANCE project is to develop, demonstrate and initiate exploitation of new concepts to enhance and train upper extremity motor function during daily-life in people with physical disabilities. We focus on two different patient groups: Stroke patients and patients with Duchenne muscular dystrophy. With the products that are finally developed from these concepts we expect that patient will enhance their self-support and, by being less dependent, improve their quality of life.

More specifically, the objectives are:

1) To develop an intelligent bidirectional multimodal adaptive interface for supporting upper extremity motor function
2) To supplement this multimodal interface with intelligence
3) To demonstrate and evaluate the motor performance of the user in interaction with this intelligent interface. The demonstration and evaluation will involve people with Duchenne Muscular Dystrophies and Stroke.
4) To prepare exploitation of the eNHANCE concepts in a Public Private Partnership (PPP) setting, involving key companies, end user communities, care institutions and knowledge institutions
5) To create strong and continuous user involvement through their involvement in user requirement identification, evaluation and dissemination of the eNHANCE concepts to stake holders and the general public

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

A controllable mechatronic arm and a hand support interface were developed. The mechatronic arm support was improved by adding additional safety features, implementation of force control. To control the mechatronic systems, a user intention detection interface was developed. A head-mounted eye tracking system has been used to detect the 3D gaze point of the subject and a wink control has been implemented. This eye tracking system was tested in combination with an UR10 robot arm. The BioServo SEMglove was integrated with the improved control system using eye-tracking and multi-modal hand sensing-based intention detection and environmental observation multimodal sensing.

Different modalities to retrieve environmental information and user status have been implemented and combined together. Such modalities include eye tracking to study the gaze behaviour of the user, mechanomyography (MMG) and electromyohraphy (EMG) to record his muscular activity, full body tracking to study the movement of the whole body, head-mounted cameras to record the scene in first perspective. In a next step, this information will be used to develop algorithms that define the user intention according to his current status and his environment. An alternative protocol for feasibility testing of a grasp support based on force sensing information was defined and five stroke patients have completed an experiment with it. Additional research was done in 11 healthy subjects to assess whether reach movements could be distinguished from reach-to-grasp movements while using inertial sensing, as a first step to investigate the potential of using movement control based on those inertial sensors for controlling the grasp support. Moreover, a Sequitur based algorithm was developed that can be used in the action grammar extraction and the intention decoding algorithm was significantly improved by developing analysis of the fovea camera.

To demonstrate and evaluate the motor performance of the user in interaction with this intelligent interface, several series of user testing will be performed. As a first step, evaluations with healthy persons were performed. Evaluations in people with stroke and Duchenne Muscular Dystrophy are being planned. An adaptive user performance model has been developed and different machine learning techniques have been tested for best performance in predicting user performance for upcoming task. This was based on experiments with 10 healthy subjects.

To create user involvement and interest, an advisory board was established, with representatives of the industrial community, associations of stroke and Duchenne patients, medical professionals in clinical rehabilitation and home care, and health insurance agencies. Members of this advisory board were involved in the user requirements identification and evaluation. Additionally, two focus groups were organised in which people with stroke and Duchenne participated.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The eNHANCE support enables the users to achieve their desired movement actions, while motivating the users to maximise their contribution. This will maximise the user performance relative to his or her personal capacity, and thus maximise the therapeutic effect. Maximising user performance requires a personalised and motivating support taking into account personal behaviour in response to arm and hand support characteristics and motivational inputs provided by the system, taking into account environment, context and social interaction. Personal behaviour will be represented by a personalised behaviour model that is adaptively identified based on the discrepancy between the performance that is actually observed and the one predicted by the model.

We foresee the following impact:

Scientific: We foresee that the eNHANCE project will contribute to new intelligent and adaptive multimodal interfaces to support people with motor impairments. These interfaces are expected to enable these people to improve their participation in society and are expected to be the basis for improving the innovation capacity of European companies in this domain, but also in much wider application domains. It is a necessary step towards context-driven support systems and towards a smart environment with a larger interaction between support systems and environment. The development of the ‘internet of things’ could be viewed in this context.

General public: eNHANCE will advance the capacity of human-machine interaction technologies to enable people with motor impairments to improve their participation in society by enabling and stimulating them to be physically active in their daily-life environment and in social interaction with other people. The potential of the technologies was explained to the general public in different interviews, and it was presented at conferences of various patient organizations.

Economy: The eNHANCE consortium includes well-chosen dynamic SME companies that have complementary exploitation plans for the eNHANCE concepts. Due to the bankruptcy of the company EyeBrain, the envisioned prototype of a low cost high performance eye tracking with improved intention detection system will not be made. Instead of this prototype we, however, envision that the company Focal Meditech may develop a prototype of a robotic arm (one of their existing products) that is controlled by an interface developed in the eNHANCE project. A first derived prototype in which eNHANCE technology was implemented was developed and presented by Focal at the Rehacare exhibition in Dusseldorf (September 28th 2016 until October 1st 2016).

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