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SENSOR-INDEPENDENT PLATFORM FOR CIRCADIAN RHYTHM ANALYSIS. Andrea Caroppo Institute for Microelectronics and Microsystems (IMM) National Research Council (CNR) c/o Campus Ecotekne , Via Monteroni , Lecce, Italy. Co-authors:
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SENSOR-INDEPENDENT PLATFORM FOR CIRCADIAN RHYTHM ANALYSIS Andrea Caroppo Institute for Microelectronics and Microsystems(IMM) National Research Council (CNR) c/o Campus Ecotekne, Via Monteroni, Lecce, Italy Co-authors: Giovanni Diraco, Gabriele Rescio, Alessandro Leone, Pietro Siciliano Institute for Microelectronics and Microsystems, (IMM) National Research Council (CNR) c/o Campus Ecotekne, Via Monteroni, Lecce, Italy
INTRODUCTION (1/3) • The need for Personal assistance with everyday activities increases with age. • Use of smart sensor technologies can help through the creation of Intelligent Environments Keeping the privacy Living in their own homes Reducing the need for assistance • Platforms of heterogeneous sensors as key technology player in AAL scenarios
INTRODUCTION (2/3) • Topic of interest: - Human Behaviour Analysis - Human Behaviour Understanding Methodologies for the detection of abnormal behavior pattern Information about user’s health and lifestyle patterns Detection of anomalies in circadian rhythm (CR) • AIM: Design and implementation of a tool for CR analysis • Automatic detection of anomalies with unsupervised methodology • Platform with sensing technologies invariant interface
INTRODUCTION (3/3) OVERVIEW OF THE PLATFORM DETECTOR LAYER Ultra-wideband Radar (UWV) Time-Of-Flight 3D Vision (TOF) MEMS-based Accelerometer (ACC) Human posture sequences SIMULATION LAYER Ground-Truth Posture Simulator Long-Term Posture Simulator Calibrated Posture Simulator REASONER LAYER CR anomalies detection Reinforcement Learning (Unsupervised Clustering) Feature Extraction
MATERIALS AND METHODS (1/5) DETECTOR LAYER POSTURE DETECTORS Human postures are detected using sensing approaches implemented with both ambient and wearable solutions PulsON 410 UWB radar MESA SR-4000 TOF sensor Smartex WWS Embedded-PC running detection algorithms
MATERIALS AND METHODS (2/5) DETECTOR LAYER COMPARISON OF THREE POSTURE DETECTORS COMMON EXPERIMENTAL SETUP FOR POSTURE RECOGNITION AND CLASSIFICATION: • 18 subjects (9 males and 9 females) • Age 38±6 years, height 175±20 cm, weight 75±22 kg • Execution of typical ADLs (household tasks, meal preparation, sitting and watching TV, relaxing and sleeping, …) • Data collected simultaneously by one TOF sensor, one UWB radar, and one MEMS accelerometer worn by each participant POSTURE CLASSIFICATION PERFORMANCE (Confusion Matrix) TOF ACC UWB
MATERIALS AND METHODS (3/5) SIMULATION LAYER Behaviour analysis long-term observations Lack of dataset containing long-term posture sequences Conceptual representation of the posture simulator
MATERIALS AND METHODS (4/5) SIMULATION LAYER CALIBRATED SIMULATION OF POSTURES Simulation of posture sequences by using a calibrated approach based on real observation given by each detector node Short term observations (actions as posture sequences) Expectation-Maximization (EM) Method Estimated parameters Calibrated Simulation for each node detector (Prediction Error Model) Model Error Modelling (MEM) Method Long-term data
MATERIALS AND METHODS (5/5) REASONER LAYER Feature Extraction Identification of sleep periods (start time and relative duration) • ADLs recognition from posture sequences using supervised methodology (HMM) • For CR, start time of actions: “going to bed” – “sleep in bed” – “wake up” are extracted for the following module: Mapping into two-dimensional space the features: 1) sleep start time (x-axis) 2) sleep duration (y-axis) … for each simulated day Reinforcement Learning (Unsupervised Clustering) Iterative K-means Clustering (K=2) for online detection of change in CR patterns A new cluster is detected if distance between centroids > TH and features extracted for N consecutive days belong to the same new cluster. N and TH settable according to physician’s indications CR anomalies detection
RESULTS (1/2) ROBUSTNESS OF CALIBRATED SIMULATION STEP For each sensor evaluation of posture classification performance MRE = Mean Relative Error by computing confusion matrix < 2% ADLs RECOGNITION STEP (7 kind of activities: sleeping, waking up, eating, cooking, housekeeping, watch TV and physical training.)
RESULTS (2/2) VALIDATION OF CLUSTERING STEP DETECTION RATE (%) OF DEVIATIONS FROM THE REFERENCE CR AT VARYING OF M AND N M Time interval in day (Reference Period) N Periods within which a change in the CR pattern was detected
CONCLUSIONS • Design and evaluation of sensor-independent platform for detection of CR anomalies • Use of abstract features (human postures) produced by a generic detector • Framework optimized for embedded processing (home application requirements: low-power consumption, noiseless and compactness) ONGOINGACTIVITIES • Classification (not only detection) of sleep disorders • Integration of other sensing technologies able to detect human postures (e.g. Kinect, …) • Ability to manage other behavior abnormalities (Sedentary Behavior, Hyperkinetic Behavior, …), adding other features (e.g. motion level or spatial position)