Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients (Preprint)

2021 ◽  
Author(s):  
Joanne Zhou ◽  
Bishal Lamichhane ◽  
Dror Ben-Zeev ◽  
Andrew Campbell ◽  
Akane Sano
Keyword(s):  
Prediction Models ◽  
Gaussian Mixture ◽  
Mobile Sensing ◽  
Cluster Models ◽  
Sensor Data ◽  
Sensing Data ◽  
Model Based ◽  
Clustering Model ◽  
Relapse Prediction ◽  
Lower Variability

BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

scholarly journals Vertical and Horizontal Queue Models for Oversaturated Signal Intersections with Quasi-Real-Time Reconstruction of Deterministic and Shockwave Queueing Profiles Using Limited Mobile Sensing Data

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Yongyang Liu ◽  
Jingqiu Guo ◽  
Yibing Wang
Keyword(s):  
Real Time ◽  
Queue Length ◽  
Real Data ◽  
Mobile Sensing ◽  
Signalized Intersections ◽  
Sensor Data ◽  
Conceptual Level ◽  
Sensing Data ◽  
Traffic Operation ◽  
Location Sensor

Deterministic/point/vertical and shockwave/physical/horizontal queueing models are widely used in traffic operation to estimate vehicular queue length and delays at bottlenecks such as signalized intersections. The consistency between the two types of queueing model in terms of their estimation performance has been a subject of debate for decades. This paper reexamines the issue, typically with respect to oversaturated signal intersections, and demonstrates the consistency based on analytical studies and microscopic simulations. While fixed-location sensor data was dominating, it was hardly possible to construct the deterministic or shockwave queueing profile using real data. For this reason, either profile had significance only at a conceptual level and could not be put into practical usage. With the quick spread of mobile sensing data, however, the situation has drastically changed. In this context, this paper also intends to develop an efficient approach to the reconstruction of the deterministic and shockwave queueing profiles in a quasi-real-time manner using very limited mobile sensing data. Microscopic simulations with AIMSUN have demonstrated the efficiency of the approach as well as the analytical results obtained in this paper.

scholarly journals Longitudinal estimation of stress-related states through bio-sensor data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vadym Mozgovoy
Keyword(s):  
Conceptual Framework ◽  
Convergent Validity ◽  
Well Being ◽  
Gaussian Mixture ◽  
Computer Applications ◽  
Cluster Models ◽  
Sensor Data ◽  
Content Type ◽  
Four Blocks ◽  
Specific Reactions

PurposeThe authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.Design/methodology/approachThe authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.FindingsThe authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.Research limitations/implicationsProposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.Practical implicationsLongitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.Social implicationsTimely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.Originality/valueThe study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 38 ◽  
Author(s):  
Martin Jänicke ◽  
Bernhard Sick ◽  
Sven Tomforde
Keyword(s):  
Activity Recognition ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Training Data ◽  
Sensor Data ◽  
Activity Data ◽  
High Loss ◽  
New Sensors ◽  
Self Adaptive

Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on the creation of a self-adaptive activity recognition system based on IMU that includes new sensors during runtime. Starting with a classifier based on GMM, the density model is adapted to new sensor data fully autonomously by issuing the marginalization property of normal distributions. To create a classifier from that, label inference is done, either based on the initial classifier or based on the training data. For evaluation, we used more than 10 h of annotated activity data from the publicly available PAMAP2 benchmark dataset. Using the data, we showed the feasibility of our approach and performed 9720 experiments, to get resilient numbers. One approach performed reasonably well, leading to a system improvement on average, with an increase in the F-score of 0.0053, while the other one shows clear drawbacks due to a high loss of information during label inference. Furthermore, a comparison with state of the art techniques shows the necessity for further experiments in this area.

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

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