scholarly journals Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3572 ◽  
Author(s):  
Nicholas C. Jacobson ◽  
Yeon Joo Chung
Keyword(s):  
Depressed Mood ◽  
Wearable Sensors ◽  
Smartphone Application ◽  
Sensor Data ◽  
Just In Time ◽  
Modeling Framework ◽  
Daily Lives ◽  
Passive Sensing ◽  
Out Of Sample ◽  
Ecological Momentary

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.

scholarly journals Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

2021 ◽  
Vol 3 ◽  
Author(s):  
Tim Kaiser ◽  
Björn Butter ◽  
Samuel Arzt ◽  
Björn Pannicke ◽  
Julia Reichenberger ◽  
...  
Keyword(s):  
Sensor Data ◽  
Food Craving ◽  
Passive Sensing ◽  
Temporal Features ◽  
Psychometric Data ◽  
Time Lagged ◽  
Feature Dimension ◽  
Ecological Momentary ◽  
Prediction Approach ◽  
Momentary Assessment

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.

scholarly journals Time-Lagged Prediction of Food Craving with Elastic Net Regression and BISCWIT: Prediction Performance of Qualitative Distinct Predictor Types

2020 ◽  
Author(s):  
Tim Kaiser ◽  
Björn Butter
Keyword(s):  
Binary Classification ◽  
Elastic Net ◽  
Sensor Data ◽  
Food Craving ◽  
Food Cravings ◽  
Passive Sensing ◽  
Psychometric Data ◽  
Time Lagged ◽  
Ecological Momentary

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from two weeks of Ecological Momentary Assessment questionnaires on stress and emotions alongside smartphone sensor data would be able to predict food cravings approximately 2.5 hours into the future in N = 46 individuals. We compared two prediction approaches (logistic BISCWIT and Elastic Net Regression (ENR). While overall prediction accuracy was low, passive sensing data alone was superior to predictions based on psychometric data and BISCWIT was superior to ENR for binary classification of FC peaks.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

Wital: WiFi-based Real-time Vital Signs Monitoring During Sleep

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Huan Yan ◽  
Zhi Liu ◽  
Fuji Ren
Keyword(s):  
Real Time ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Implementation Process ◽  
Sleep Monitoring ◽  
Daily Lives ◽  
Vital Signs Monitoring ◽  
Quality Sleep ◽  
Enhancement Method ◽  
Positioning Algorithm

High-quality sleep is essential to our daily lives, and real-time monitoring of vital signs during sleep is beneficial. Current sleep monitoring solutions are mostly based on wearable sensors or cameras, the former is worse for sleep quality, the latter is worse for privacy, dissimilar to such methods, we implement our sleep monitoring system based on COTS WiFi devices. There are two challenges need to be overcome in the system implementation process: First, the torso deformation caused by breathing/heartbeat is weak, how to effectively capture this deformation? Second, movements such as turning over will affect the accuracy of vital signs monitoring, how to quickly distinguish such movements? For the former, we propose a motion detection capability enhancement method based on Rice-K theory and Fresnel theory. For the latter, we propose a sleep motion positioning algorithm based on regularity detection. The experimental results indicated the performance of our method.

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.

scholarly journals Measuring social networks in primates: wearable sensors versus direct observations

2020 ◽  
Vol 476 (2236) ◽  
pp. 20190737
Author(s):  
Valeria Gelardi ◽  
Jeanne Godard ◽  
Dany Paleressompoulle ◽  
Nicolas Claidiere ◽  
Alain Barrat
Keyword(s):  
Data Collection ◽  
Wearable Sensors ◽  
A Priori ◽  
Population Level ◽  
Sensor Data ◽  
Collection Method ◽  
Data Collection Method ◽  
The Social ◽  
Different Types ◽  
Data Yield

Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.

scholarly journals Awake Bruxism – Definition Through Self-report and Through Ecological Momentary Assessment

2021 ◽  
Author(s):  
Alona Emodi-Perlman ◽  
Daniele Manfrendini ◽  
Tamar Shalev-Antsel ◽  
Ilanit Yevdayev ◽  
Pessia Frideman-Rubin ◽  
...  
Keyword(s):  
Ecological Momentary Assessment ◽  
Single Point ◽  
Smartphone Application ◽  
Self Report ◽  
Teeth Grinding ◽  
Teeth Clenching ◽  
The Status ◽  
Ecological Momentary ◽  
Awake Bruxism ◽  
Momentary Assessment

Diagnosis of Awake Bruxism (AB) is problematic due to the inability to use continuous recordings during daytime activities. Recently, a new semi-instrumental approach was suggested, viz., an Ecological Momentary Assessment (EMA), with the use of a smartphone application. With the application subjects are requested to report, at least 12 times per day, the status of their masticatory muscle activity (relaxed muscles, jaw bracing without tooth contact, teeth contact, teeth clenching or teeth grinding). The aim of the present study was to compare the EMA to the assessment of AB as defined by a single point self-report. The most frequent condition recorded by the EMA was relaxed muscles (ca. 60%) and the least frequent one - Teeth grinding (0.6 %). The relaxed muscle condition also showed the lowest coefficient of variance over a 7day period of report. Additionally, only the relaxed muscles and the Jaw bracing conditions presented an acceptable ability to discriminate between AB positive and AB negative subjects, as defined by single point self-report questions. The combination between self-report and EMA may have a potential to promote our ability to diagnose AB. We suggest to re-consider the conditions of Teeth contact and Teeth grinding while using EMA to evaluate AB.

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