Sleep Activity Recognition using Binary Motion Sensors

International audienceEarly detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect poten- tial sleep disturbances of the monitored senior res- idents. We use an unsupervised inference method based on actigraphy data generated by ambient mo- tion sensors scattered around the senior’s apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates

Une Approche Bayésienne pour la reconnaissance des périodes de sommeil à l'aide de capteurs de mouvement

National audienceLe vieillissement de la population confronte les sociétés modernes à une transformation démographique sans précédent qui ne va pas sans poser de nombreux problèmes. Parmi ceux-ci, il y a le déséquilibre de nos systèmes de retraite, et le coût que va engendrer la prise en charge de la dépendance des plus âgés. Sur ce dernier point, outre les aspects économiques, le placement des personnes âgées n’est bien souvent qu’un choix de raison et peut être assez mal vécu par les personnes. Une réponse à cette problématique sociétale est le développement des technologies qui facilitent le maintien à domicile des personnes âgées. L’état de l’art du domaine regorge de projets amont qui vont dans ce sens. Parmi ceux-ci beaucoup cherchent à développer des systèmes de télésurveillance à domicile. Leurs objectifs sont de détecter, voire de prévenir l’occurrence de situations inquiétantes ou critiques et d’évaluer l’état physique voire la fragilité des personnes suivies. C’est dans ce cadre que se situe cette contribution. Nous nous focaliserons dans cet article sur le problème particulier du suivi de la qualité du sommeil ainsi qu’à la détection des levés nocturnes d’une personne vivant seule à son domicile. Celui-ci est équipé de capteurs ambiants simples tel que des détecteurs de mouvement binaires. Nous présenterons une méthode d’inférence bayésienne qui permet à notre solution d’être assez flexible et robuste aux différents types d’installation et configuration d’appartements tout en maintenant une précision de prédiction de 0.94. Cette solution est en cours de déploiement sur plusieurs dizaines d’appartements en Lorraine

Learning Longterm Representations for Person Re-Identification Using Radio Signals

Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. Existing ReID methods rely on images or videos collected using RGB cameras. They extract appearance features like clothes, shoes, hair, etc. Such features, however, can change drastically from one day to the next, leading to inability to identify people over extended time periods. In this paper, we introduce RF-ReID, a novel approach that harnesses radio frequency (RF) signals for longterm person ReID. RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape. We evaluate the performance of RF-ReID on longitudinal datasets that span days and weeks, where the person may wear different clothes across days. Our experiments demonstrate that RF-ReID outperforms state-of-the-art RGB-based ReID approaches for long term person ReID. Our results also reveal two interesting features: First since RF signals work in the presence of occlusions and poor lighting, RF-ReID allows for person ReID in such scenarios. Second, unlike photos and videos which reveal personal and private information, RF signals are more privacy-preserving, and hence can help extend person ReID to privacy-concerned domains, like healthcare.Comment: CVPR 2020. The first three authors contributed equally to this pape

In-Home Daily-Life Captioning Using Radio Signals

This paper aims to caption daily life --i.e., to create a textual description of people's activities and interactions with objects in their homes. Addressing this problem requires novel methods beyond traditional video captioning, as most people would have privacy concerns about deploying cameras throughout their homes. We introduce RF-Diary, a new model for captioning daily life by analyzing the privacy-preserving radio signal in the home with the home's floormap. RF-Diary can further observe and caption people's life through walls and occlusions and in dark settings. In designing RF-Diary, we exploit the ability of radio signals to capture people's 3D dynamics, and use the floormap to help the model learn people's interactions with objects. We also use a multi-modal feature alignment training scheme that leverages existing video-based captioning datasets to improve the performance of our radio-based captioning model. Extensive experimental results demonstrate that RF-Diary generates accurate captions under visible conditions. It also sustains its good performance in dark or occluded settings, where video-based captioning approaches fail to generate meaningful captions. For more information, please visit our project webpage: http://rf-diary.csail.mit.eduComment: ECCV 2020. The first two authors contributed equally to this pape

WiMorse: a contactless Morse code text input system using ambient WiFi signals

International audienceRecent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with Motor Neurone Disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus we propose to develop a Morse code based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the Channel State Information (CSI) between two antennas to enhance the Signal to Noise Ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity

The effective and ethical development of artificial intelligence: An opportunity to improve our wellbeing

This project has been supported by the Australian Government through the Australian Research Council (project number CS170100008); the Department of Industry, Innovation and Science; and the Department of Prime Minister and Cabinet. ACOLA collaborates with the Australian Academy of Health and Medical Sciences and the New Zealand Royal Society Te Apārangi to deliver the interdisciplinary Horizon Scanning reports to government. The aims of the project which produced this report are: 1. Examine the transformative role that artificial intelligence may play in different sectors of the economy, including the opportunities, risks and challenges that advancement presents. 2. Examine the ethical, legal and social considerations and frameworks required to enable and support broad development and uptake of artificial intelligence. 3. Assess the future education, skills and infrastructure requirements to manage workforce transition and support thriving and internationally competitive artificial intelligence industries