Personalized Posture and Fall Classification with Shallow Gated Recurrent Units

Link to final publication : https://ieeexplore.ieee.org/document/8787455International audienceActivities of Daily Living (ADL) classification is a key part of assisted living systems as it can be used to assess a person autonomy. We present in this paper an activity classification pipeline using Gated Recurrent Units (GRU) and inertial sequences. We aim to take advantage of the feature extraction properties of neural networks to free ourselves from defining rules or manually choosing features. We also investigate the advantages of resampling input sequences and personalizing GRU models to improve the performances. We evaluate our models on two datasets: a dataset containing five common postures: sitting, lying, standing, walking and transfer and a dataset named MobiAct V2 providing ADL and falls. Results show that the proposed approach could benefit eHealth services and particularly activity monitoring

Utilization of mechanical power and associations with clinical outcomes in brain injured patients: a secondary analysis of the extubation strategies in neuro-intensive care unit patients and associations with outcome (ENIO) trial

Background: There is insufficient evidence to guide ventilatory targets in acute brain injury (ABI). Recent studies have shown associations between mechanical power (MP) and mortality in critical care populations. We aimed to describe MP in ventilated patients with ABI, and evaluate associations between MP and clinical outcomes. Methods: In this preplanned, secondary analysis of a prospective, multi-center, observational cohort study (ENIO, NCT03400904), we included adult patients with ABI (Glasgow Coma Scale ≤ 12 before intubation) who required mechanical ventilation (MV) ≥ 24 h. Using multivariable log binomial regressions, we separately assessed associations between MP on hospital day (HD)1, HD3, HD7 and clinical outcomes: hospital mortality, need for reintubation, tracheostomy placement, and development of acute respiratory distress syndrome (ARDS). Results: We included 1217 patients (mean age 51.2 years [SD 18.1], 66% male, mean body mass index [BMI] 26.3 [SD 5.18]) hospitalized at 62 intensive care units in 18 countries. Hospital mortality was 11% (n = 139), 44% (n = 536) were extubated by HD7 of which 20% (107/536) required reintubation, 28% (n = 340) underwent tracheostomy placement, and 9% (n = 114) developed ARDS. The median MP on HD1, HD3, and HD7 was 11.9 J/min [IQR 9.2-15.1], 13 J/min [IQR 10-17], and 14 J/min [IQR 11-20], respectively. MP was overall higher in patients with ARDS, especially those with higher ARDS severity. After controlling for same-day pressure of arterial oxygen/fraction of inspired oxygen (P/F ratio), BMI, and neurological severity, MP at HD1, HD3, and HD7 was independently associated with hospital mortality, reintubation and tracheostomy placement. The adjusted relative risk (aRR) was greater at higher MP, and strongest for: mortality on HD1 (compared to the HD1 median MP 11.9 J/min, aRR at 17 J/min was 1.22, 95% CI 1.14-1.30) and HD3 (1.38, 95% CI 1.23-1.53), reintubation on HD1 (1.64; 95% CI 1.57-1.72), and tracheostomy on HD7 (1.53; 95%CI 1.18-1.99). MP was associated with the development of moderate-severe ARDS on HD1 (2.07; 95% CI 1.56-2.78) and HD3 (1.76; 95% CI 1.41-2.22). Conclusions: Exposure to high MP during the first week of MV is associated with poor clinical outcomes in ABI, independent of P/F ratio and neurological severity. Potential benefits of optimizing ventilator settings to limit MP warrant further investigation

Alien Registration- Compagnon, Paul (Waterville, Kennebec County)

https://digitalmaine.com/alien_docs/15477/thumbnail.jp

Alien Registration- Compagnon, Paul (Waterville, Kennebec County)

https://digitalmaine.com/alien_docs/15477/thumbnail.jp

Apprentissage de métrique sur les séquences : Application à la reconnaissance d'activité

This thesis has been realized thanks to a cifre between Orange labs Grenoble and the LIRIS at Lyon. This thesis aims at proposing new neural network approaches to retrieve the habitual behaviors of fragile people in order to provide them with a monitoring at home while respecting their privacy and avoiding stigmatization. In this perspective, we concentrate on the exploitation of wearable motion sensor data (accelerometer, gyrometer, magnetometer, barometer etc.) which are nowadays easily embedded into smartphones and smartwatches. In a first contribution, we propose to employ few-shot learning with an architecture called matching network to learn a personalized and flexible activity recognition model. This model learn to recognize a new class from just one or few new samples since it matches rather than classify. Therefore this model allows to better handle the large variety of activities one can do in one day while alleviating the burden of data labeling. In a second part, we advocate for a change of perspectives by proposing to retrieve recurrent unlabeled activity patterns called routines instead of precise activities. We propose a formalization of the concept of routine with the notion of almost-periodic functions which prompts us to employ sequence metric learning. We propose a neural network architecture based on robust sequence representation learning with a Sequence-to-Sequence model and metric learning with a siamese network. No activity labels are used to train the model by setting up an equivalence constraint with the data timestamps. We propose to identify the routines with a spectral clustering and to evaluate the whole routine retrieval process with information-theoretic clustering scores. The last contribution of this thesis is a new neural network model for sequence metric learning called Coupled Gated Recurrent Unit. This model has been conceived by taking inspiration from the dynamical system theory and notably the concept of synchronization. We propose to improve the siamese gating recurrent unit architecture by implementing a coupling which should allow it to better process the hard samples. We finally experiment this architecture to recognize activities and retrieve routines.Cette thèse est réalisée dans le cadre d'une cifre entre Orange labs Grenoble et le laboratoire LIRIS à Lyon. Elle a pour objet d'étude la conception d'algorithmes de machine learning pour reconnaitre les comportements habituels des usagers (routines), ceci à des fins de suivi médical à domicile des personnes fragiles en respectant leur vie privée et en évitant la stigmatisation. Pour cela, nous nous concentrons sur l'exploitation des données produites par des capteurs de mouvement (accéléromètre, gyroscope magnétomètre, etc.) portés présents dans les téléphones mais aussi dans les montres connectées, des objets de la vie quotidienne. Dans une première partie, nous proposons d'utiliser un modèle de few-shot learning (matching network) pour apprendre un modèle personnalisé et flexible de reconnaissance d'activités à partir de peu de données. Ce modèle s'adapte à partir de quelques exemples à une nouvelles classe et permet donc de mieux gérer les activités très variées qu'une personne peut réaliser dans une journée. Dans une seconde partie, nous proposons une formalisation mathématique du concept de routine et nous en déduisons une méthode de reconnaissance de celles-ci grâce à l'apprentissage de métrique. Nous proposons un modèle qui combine apprentissage de représentation avec un modèle "Sequence to Sequence" et apprentissage de métrique comme un réseau de neurones siamois. Le modèle est appris sans étiquette d'activité uniquement grâce à l'horodatage des données. Nous proposons ensuite d'identifier les routines grâce à un clustering et les scores associés. Dans la dernière partie, nous proposons un nouveau modèle de réseau de neurones siamois récurrents dit couplés. Ce modèle a été conçu en s'inspirant de la théorie des systèmes dynamiques dans le but d'améliorer l'architecture "siamese GRU", notamment en ce qui concerne les exemples difficiles (hard positive/negative samples). Nous expérimentons cette architecture en reconnaissance d'activités et de routines

Sciences sociales et ballond rond

Introduction (avec Paul Dietschy) du numéro spécial "Football, sport mondial et sociétés locales"International audienc

Sciences sociales et ballond rond

Introduction (avec Paul Dietschy) du numéro spécial "Football, sport mondial et sociétés locales"International audienc

Learning Personalized ADL Recognition Models from Few Raw Data

International audienceRecognition of Activities of Daily Living (ADL) is an essential component of assisted living systems based on actigraphy. This task can nowadays be performed by machine learning models which are able to automatically extract and learn relevant features but, most of time, need to be trained with large amounts of data collected on several users. In this paper, we propose an approach to learn personalized ADL recognition models from few raw data based on a specific type of neural network called matching network. The interest of this few-shot learning approach is threefold. Firstly, people perform activities their own way and general models may average out important individual characteristics unlike personalized models that could thus achieve better performance. Secondly, gathering large quantities of annotated data from one user is time-consuming and threatens privacy in a medical context. Thirdly, matching networks are by nature weakly dependent on the classes they are trained on and can generalize easily to new activities without needing extra training, thus making them very versatile for real applications. Our results show the effectiveness of the proposed approach compared to general neural network models, even in situations with few training data