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This innovative platform aims to detect anomalies in circadian rhythms using diverse sensors to analyze human behaviors and health patterns. The tool offers automatic anomaly detection and a simulation layer for accurate posture recognition and long-term behavior analysis. The platform includes a reasoner layer for feature extraction and reinforcement learning for clustering patterns. The research focuses on robustness, activity recognition, and clustering accuracy, showcasing potential in monitoring and managing behavioral abnormalities in home environments.
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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)
