Preserving the autonomy of elderlies or of people affected by a neuro-evolutionary disease such as Alzheimer's, and relieving the mental burden on their caregivers, is an ethical, economic, and social challenge for society.To address this issue, an interdisciplinary consortium involving medical experts, researchers and end-users has been created to co-design and develop a socio-technical construct named Mementop.The solution aimed at the entire ecosystem of the care receiver -i.e., his caregivers and health professionals -, bringing together in a unified user experience the features required by these parties while allowing the care receiver to remain actor in his daily life.Mementop is a certified medical device based on a smartphone application shared by all the contributors of this senior-centred ecosystem, a multi-sensory and customisable printed press product as well as a set of digital inclusion aids.The first version is available since early 2022 and research is conducted to improve its acceptability.Other ongoing research deal with advanced AI techniques to create an intelligent companion based on a predictive model of the evolution of the loss of autonomy.
People living with spinal cord injury (SCI) suffer from paralysis of their back and lower limbs muscles, which negatively impacts the control of their sitting position.Also, instead of using their back and abdominal muscles, which naturally are engaged to stabilize a sitting position, these individuals engage their upper limbs to maintain stability.This paper presents a model-based approach to better understand which strategies contribute to postural control in a sitting position.The proposed model is written in the form of a nonlinear descriptor system.The main difficulty is to be able to reconstruct joint torques which are unmeasurable.An unknown input observer is synthesized in order to estimate these unmeasurable torques.Results issued from simulation and real tests data are presented in order to underline the validity of the approach for estimating postural control parameters which could allow, in future developments, to better qualify the contribution of the upper limbs in rehabilitation exercises.
Visual neuroprostheses are devices that restore limited visual perception for visually impaired patients.Some of these neuroprostheses, implanted in the retina or the visual cortex, include an implant, a computing device and an external camera to capture the scene.Microstimulation of the retina or the visual cortex activates white spots called phosphenes, which restores a limited visual perception.Indeed, the resolution of current implants (i.e., the number and spacing of the electrodes) that have passed clinical trial phases remains low.Such a low resolution, coupled with the limited number of different colours rendered by the implants, limits the information that can be transferred.Hence, the regular rendering process used in the implants, called scoreboard, is insufficient to provide implanted patients with a good understanding of the visual scene and complex sensorimotor processes such as wayfinding.Ongoing research mainly aims at maximising the quality and quantity of the phosphenes provided by the implant.We set up a comparative study between different renderings, and we showed that providing the implanted patient with the possibility to switch between different renderings significantly increases the understanding of the environment.
Idiopathic scoliosis is a tri-dimensional deformation of the spine of unknown causes, impacting 2 to 3% of the population.It's a chronic pathology starting during childhood or adolescence and it is associated to long term physical and psychological prejudices for the patient, leading to important healthcare costs for the system.The weight of these consequences is linked to the early diagnostic of the pathology and to the subsequent treatment.Therefore, the early screening of AIS is of major importance.In France, the actual screening technique is clinical (Adam's test), it's a technique reserved to personnel of the medical field and with an imperfect sensibility (71-84% depending on the literature).The final goal of our study is to develop a non-ionizing, automatic evaluation tool of the idiopathic scoliosis, that would allow an accurate screening and if possible to evaluate its severity.The preliminary work conducted with this goal is presented in this paper.After a first literature review concerning the idiopathic scoliosis, the depth camera Microsoft Kinect V2 is used to generate a tridimensional point cloud of the back's surface of the patient.This point cloud is computed with the objective of quantifying an asymmetry of the patient's back, indicator of the spine's deformation in the axial plan, a specificity of the scoliosis.The two main axes of work were about the guarantee of the data's reproducibility and the choice of a pertinent parameter for the back's surface asymmetry, based on clinical and physio-pathological hypotheses and on the available literature data.The outcome of this preliminary work is a novelty surface parameter acquired in a patient-related coordinate system, with the goal to maximize its reproducibility.The first results on a few healthy patients and a scoliotic patient were encouraging.This work will obviously need to be followed by a validation study on a larger cohort of healthy and scoliotic patient.A key objective in recommending assistive technologies is to promote personal independence, social participation and life-building.
The Leap Motion Controller is a small hand motion capture device that has been the subject of numerous studies since it was launched in 2013.This sensor is particularly used in the field of disability, diagnosis or functional rehabilitation.This article presents two preliminary studies on the use of this device as a gesture mouse and then as a gesture controller.If the Leap Motion Controller is simple to implement, its lack of precision in some gesture configurations and of reliability (outliers) is a barrier which could certainly be lifted in the context of a multimodal interaction.
In the PRIM project, we aim at giving people the power to create scenagrams (interaction scenarios between a human and digital devices) without the need to learn programming or to ask for computer scientists. In this project, software design follows an unconventional approach, far from classical codes, to embody human thinking (based on interactions) instead of computer logic (based on algorithms). The main idea rests on a new time representation using a PRIM-specific timeline instead of a standardized timeline. We evaluated acceptability and cognitive compatibility of this new timeline with 50 participants. Results are very promising. In this paper, we detail qualitative evaluation results about the interest of such software in the field of disability.
In this article, we present our work regarding the development of advanced driver-assistance systems for an electric-powered wheelchair. Our project aims at improving the autonomy of people with reduced mobility. After conducting a clinical study, we identified several use-cases. In this paper, we introduce the detection, localization and tracking of points of interest in the immediate surroundings of the chair in an indoor environment, i.e.: doors, handles, light switches, etc. The aim is not only to improve perception around the chair but also to enable semi-autonomous driving towards these targets. First, we introduce a repurposing of YOLOv3, the object detection algorithm, to our use case. Then, we show our use of the Intel Realsense camera for depth estimation. Finally, we describe our adaptation of the SORT algorithm to track 3D interest points. To validate our approach, we realized several experiments in a controlled indoor environment. The detection, distance estimation, and tracking pipeline is tested using our custom dataset. This includes corridors, doors, handles, and switches. One of the scenarios studied to validate the proposed platform includes not only the detection and tracking of objects but also the movement of the wheelchair towards one of these points of interest.
Rehabilitation is essential to Multiple Sclerosis (MS) treatment. Its efficacy can be maintained by self-rehabilitation but its execution often remains unpredictable. A mobile application could help people with MS (pwMS) practice these self-rehabilitation exercises at home. We are reporting the results of a study aiming to guide the design of a mobile application that would motivate pwMS to do so. With this aim in mind, a study has been conducted among the rehabilitation staff who work with these people. The results are steering the design of the application towards an occupation-centric application, in addition with some playful elements in order to provoke interest and commitment.
This article presents a heterogeneous, open and configurable data recording device to facilitate the implementation of clinical trials. It includes an embedded system connected via USB to an Android device, and a database in the Cloud. Using an Android application, it is possible to configure each clinical trial according to the needs of the test protocol and the people involved in the study. This device ensures the recording and storage of data from both the various sensors used and the questionnaires. This original device was used during the implementation of the clinical trials for a powered wheelchair “Obstacle Alerting System”. This system will be mounted on the wheelchairs of the people participating in the clinical trials and used for 14 days in their real life environment.
Autonomy and the ability to maintain social activities can be challenging for people with disabilities experiencing reduced mobility. In the case of disabilities that impact mobility, power wheelchairs can help such people retain or regain autonomy. Nonetheless, driving a power wheelchair is a complex task that requires a combination of cognitive, visual and visuo-spatial abilities. In practice, people need to pass prior ability tests and driving training before being prescribed a power wheelchair by their therapist. Still, conventional training in occupational therapy can be insufficient for some people with severe cognitive and/or visio-spatial functions. As such, these people are often prevented from obtaining a power wheelchair prescription from their therapist due to safety concerns. In this context, driving simulators might be efficient and promising tools to provide alternative, adaptive, flexible, and safe training. In previous work, we proposed a Virtual Reality (VR) driving simula-integrating vestibular feedback to simulate wheelchair motion sensations. The performance and acceptability of a VR simulator rely on satisfying user Quality of Experience (QoE). Therefore, our simulator is designed to give the user a high Sense of Presence (SoP) and low Cyber-sickness. This paper presents a pilot study assessing the impact of the vestibular feedback provided on user QoE. Participants were asked to perform a driving task whilst in the simulator under two conditions: with and without vestibular feedback. User QoE is assessed through subjective questionnaires measuring user SoP and cyber-sickness. The results show that vestibular feedback activation increases SoP and decreases cyber-sickness. This study constitutes a mandatory step before clinical trials and, as such, only enrolled people without disabilities.
The cochlear implant is the most successful implantable device for the rehabilitation of profound deafness. However, in some cases, the electrical stimulation delivered by the electrode can spread inside the cochlea creating overlap and interaction between frequency channels. By using channel-selection algorithms like the “nofm” coding-strategy, channel interaction can be reduced. This paper describes the preliminary results of experiments conducted with normal hearing subjects (n = 9). Using a vocoder, the present study simulated the hearing through a cochlear implant. Speech understanding in noise was measured by varying the number of selected channels (“nofm”: 4, 8, 12 and 16of20) and the degree of simulated channel interaction (“Low”, “Medium”, “High”). Also, with the vocoder, we evaluated the impact of simulated channel interaction on frequency selectivity by measuring psychoacoustic tuning curves. The results showed a significant average effect of the signal-to-noise ratio (p < 0.0001), the degree of channel interaction (p < 0.0001) and the number of selected channels, (p = 0.029). The highest degree of channel interaction significantly decreases intelligibility as well as frequency selectivity. These results underline the importance of measuring channel interaction for cochlear implanted patients to have a prognostic test and to adjust fitting methods in consequence. The next step of this project will be to transpose these experiments to implant users, to support our results.
This work describes a ‘detection of abnormal activities and health-related changes’ system for an elderly person at her/his home. The analysis is based on the data collected by a domotic box of the market. The box was initially designed to continuously recognize the owner’s daily activities in order to anticipate anomalies and consequently prevent health complications and enhance the rate of disease prevention. The box uses non-intrusive home automation sensors to detect the activity level of the occupants. It is equipped also with other technologies, including humidity sensors, bed and chair sensors to name a few. In order to build a system capable of intercepting warning signs for early intervention, we adopt a Hidden Markov Model based approach that we will initialize beforehand with the activity sequences of the user within a given period. The outcomes of the model paves the way for deducting the final judgement and reporting a relevant context-aware alert to healthcare service experts. Other statistical processes might complete this behavioural analysis later on to enhance the alerts accuracy.
Presentation and analysis of the first experimentation of autonomous navigation, for a young man suffering from category 4 blindness, associated with disorders of the sensitivity of the limbs, moving thanks to the feelings of the accelerations and decelerations of an anti-collision and anti-fall electric wheelchair.
MemoRob is a model about how to optimize the use of robots for learning. It is based on a list of each possible robotic source of distraction associated with its relevant effects according to its nature and to the target learning mode. While collecting the sources of distraction that the robotics literature provides, the instantiation method of pairing each source and each learning mode with the nature of the distraction as well as its distracting effects allows to consider how to remedy these effects of robotic distraction effects although still having the robotic input as a learning medium. In this article, we provide the motivations that led to the need for the MemoRob model, the list of sources and effects generated by the Human-Robot interaction that may interfere with learning situations, the learning modes described according to their processes and mechanisms and, finally, a set of predictions on whether a given robotic learning situations might promote attention or distraction.
Making geometry accessible for blind people, apart from the formal aspects, can pose some difficulties, especially in terms of accessibility to figures. To deal with this problem this article focuses on paper folding where both Euclidean and origami axiomatic systems are used simultaneously. In the first case, with a ruler and compass, we can solve quadratic problems in a plane. In addition, the axioms of origami allow us to address unanswered questions with classical geometry methods, which involve cubic equations, such as the trisection of an angle. An experiment with INJA (National Institute for Blind Youth, Paris) students and other blind people will take place so that we can see the possibilities offered by this method, which brings a ludic, but rigorous approach to these complex and frequently off-putting issues. We believe that this dynamic pedagogical approach can increase interest and motivation, encourage tactile stimulation and facilitate the development of specific structures of brain plasticity. The article is written in a linear way, accessible to blind people; figures are provided to facilitate understanding for "visually impaired" people, who are not used to following a geometric concept without pictures. Finally, it should be noted that the method is particularly suitable in an inclusive education context.
Introduction: Psychiatric trainees lack psychotherapy training. In 2018, the European Psychiatric Association (EPA) offered a Massive Open Online Course (MOOC), “Introduction to Cognitive Behavioural Therapy”. We will present an overview of the course participants and engagement metrics, and propose improvements for future courses. Methods: Non-identifiable participant information was collected through a welcome survey. Additional data were internally extracted, including demographic information, interests, educational background, learning objectives, completion and satisfaction rate. Engagement strategy questionnaires were distributed and screen time calculated. Results: There were 7.116 enrollments, 1.504 completed the welcome survey and 954 (13%) the overall course. The three most represented countries were: Greece (18%), France (8%) and the UK (7%). Psychologists and psychology students represented 41% of the initial participants, psychiatrists and psychiatry trainees 33%. Conclusion: The MOOC achieved its primary goals and received a wide acceptance. The dropout rate was lower than similar online courses. Improvements and amendments are suggested.
Geostatistics plays an important role for reserve estimation in mining industry. Geostatistical tools became popular because of its high degree of accuracy and time saving process for estimation. The uncertainty of geological deposit can be populated by geo-statistical tools. The limestone ore deposit was studied in this paper. The assay value of individual constituents of limestone ore i.e CaO, SiO2, Al2O3 and Fe2O3 were determined for a block by using Inverse Square Distance Weighting (ISDW) method. The average assay value of those individual constituents were 45.85, 15.94, 1.56 and 0.82 percentage respectively. The assay value of CaO was also estimated by two linear method of estimation i.e ISDW and Ordinary Kriging (OK). The assay value of CaO were determined 45.85 and 44.67 percentage respectively. The assay values were properly validated and concluded accordingly. The application of ISDW and OK were implemented to build the resource model together in order to assess the uncertainty of the deposit. Grade estimation by using different geo-statistical techniques are done by SURPAC mine planning software.
One of the key challenges for the training of healthcare professionals (HCPs) is to maintain a good understanding and knowledge of new assistive technologies (ATs) that are currently on the market [1]. Indeed, at present, available training on ATs is limited and does not meet the practice-related needs of HCPs. It is in this context that the ADAPT European project aims to develop a new AT training programme for healthcare professionals, which will also introduce them to the project’s new AT developments - a smart powered wheelchair and a virtual reality wheelchair-driving simulator. The program consists of six multimodal units; five delivered via e-learning and one through a blended method of e-learning and face-to-face sessions. The development of the content is guided by findings from an earlier literature review and an online survey of AT training needs for HCP’s, both undertaken by the ADAPT cross-national research team, comprised of UK and French experts. The level of technical difficulty increases with successive units in order to train all HCPs to use innovative ATs more widely in their practice. A Learning Management System enables the dissemination of the e-learning AT program. Preliminary results from participant unit-specific evaluations available at this stage are overall positive and encouraging.
