Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar

Recognizing Stereotyped Behavior in Children with Autism

This project works on helping in identifying and recognizing autistic children's stereotyped behaviors, which can help in diagnosing autism on children. The recognition accomplished by building a signal processing model that collects data from a smartwatch equipped with a gyroscope and accelerometer in order to produce a feature vector of 316 features. This feature vector is used to choose a predictive model with the highest accuracy, which is Ridge classifier in this project. The results show that those common stereotype behaviors could be recognized using the Ridge machine learning algorithm with overall average accuracy ranges between 98.7% to 99.5 %. For hand flapping, head banging, and running back and forth, the overall precision ranges between 98% to 100 %, overall recall ranges between 98% to 100 %, overall F1-score ranges between 98% to 100 % and overall macro, weighted and micro averages is 99 %. This Ridge classifier used to implement a real-time application developed on a smartphone (iPhone) to detect the stereotyped behaviors for autistic children who are wearing the smartwatch (Apple watch

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

An Observational Study With the Janssen Autism Knowledge Engine (JAKE®) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components.Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures.Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was “easy” (69%, 74/108) or “very easy” (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93–100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported.Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT0266899

Assessment e riabilitazione delle abilità sensomotorie e del linguaggio nell'autismo tramite l'utilizzo di interfacce tangibili e sistemi di analisi computazionale

Il presente lavoro descrive un percorso metodologico che mira ad intrecciare vari approcci e costrutti che avvolgono la sfera clinica dei disturbi dello spettro autistico (ASD) al fine di riuscire ad arricchire, in maniera dinamica ed innovativa, i processi di assessment diagnosi e riabilitazione che la caratterizzano. A partire da due approcci apparentemente lontani, l'Embodied Cognition e l' analisi del comportamento applicata (Applied Behavior Analysis), illustreremo come, avvalendoci delle potenzialità delle nuove tecnologie, possiamo ottenere nuove misure oggettive del disturbo e ridefinire i percorsi di riabilitazione a partire da esse. A partire dai costrutti teorici dell'Embodied Cognition abbiamo immaginato e sviluppato degli strumenti ad hoc per il rilevamento di pattern di movimento specifici. Servendoci dei principi metodologici dell'Applied Behavior Analysis, il lavoro illustra nuovi modi di affrontare la riabilitazione nell'ambito dell'autismo. Dividendo il lavoro in tre sessioni sperimentali il lavoro illustra tre aspetti distinti ma consequenziali dell'approccio all'autismo secondo questa nuova ottica: La prima sessione sperimentale è dedicata all'implementazione e alla verifica dell'efficacia di un nuovo strumento di comunicazione aumentativa alternativa (CAA) LI-AR, studiato ad hoc per riabilitare il comportamento comunicativo nell'autismo. La seconda sessione sperimentale descrive le potenzialità di un nuovo strumento per il rilevamento di pattern tipici di movimento nell'autismo. La terza sessione unisce gli approcci e propone l'utilizzo, in ambito riabilitativo, di un nuovo strumento che sfruttando una metodologia basata sul condizionamento classico e quindi di natura comportamentista, mira a riabilitare un aspetto trasparente del comportamento, ovvero, la propriocezione e i rapporti di causalità tra corpo e mondo esterno, tipicamente alterati in ASD. Lo studio spinge a ripensare ai modi di affrontare l'autismo senza perdere di vista la storia che ha caratterizzato la clinica del disturbo, ma anzi, a partire da essa, con l'obiettivo di potenziare i costrutti comportamentisti, ad oggi di elezione per il trattamento della sindrome, intrecciandoli ad approcci cognitivisti e cercando di sfruttare al massimo i vantaggi che oggi la tecnologia e i sistemi di intelligenza artificiale possono fornirci

Recursive Behavior Recording: Complex Motor Stereotypies and Anatomical Behavior Descriptions

A novel anatomical behavioral descriptive taxonomy improves motion capture in complex motor stereotypies (CMS) by indexing precise time data without degradation in the complexity of whole body movement in CMS. The absence of etiological explanation of complex motor stereotypies warrants the aggregation of a core CMS dataset to compare regulation of repetitive behaviors in the time domain. A set of visual formalisms trap configurations of behavioral markers (lateralized movements) for behavioral phenotype discovery as paired transitions (from, to) and asymmetries within repetitive restrictive behaviors. This translational project integrates NIH MeSH (medical subject headings) taxonomy with direct biological interface (wearable sensors and nanoscience in vitro assays) to design the architecture for exploratory diagnostic instruments. Motion capture technology when calibrated to multi-resolution indexing system (MeSH based) quantifies potential diagnostic criteria for comparing severity of CMS within behavioral plasticity and switching (sustained repetition or cyclic repetition) time-signatures. Diagnostic instruments sensitive to high behavioral resolution promote measurement to maximize behavioral activity while minimizing biological uncertainty. A novel protocol advances CMS research through instruments with recursive design