scholarly journals The importance of behavior theory in control system modeling of physical activity sensor data

2014 ◽  
Author(s):  
William T. Riley ◽  
Cesar A. Martin ◽  
Daniel E. Rivera
Keyword(s):  
Physical Activity ◽  
Control System ◽  
System Modeling ◽  
Sensor Data ◽  
Behavior Theory

Getting Enough Sleep: On the Importance of Collecting Longitudinal Data from Wearables to Assess Sleep Quality and Seasonal Effects on Variability in Daily Activity (Preprint)

2021 ◽  
Author(s):  
María Óskarsdóttir ◽  
Anna Sigridur Islind ◽  
Elias August ◽  
Erna Sif Arnardóttir ◽  
Francois Patou ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Data Collection ◽  
Sleep Quality ◽  
Daily Activity ◽  
Hospital Environment ◽  
Sensor Data ◽  
Seasonal Patterns ◽  
Sleep Patterns ◽  
Sleep Habits

BACKGROUND The method considered the gold standard for recording sleep is a polysomnography, where the measurement is performed in a hospital environment for 1-3 nights. This requires subjects to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. For longer studies with actigraphy, 3-14 days of data collection is typically used for both clinical and research studies. OBJECTIVE The primary goal of this paper is to investigate if the aforementioned timespan is sufficient for data collection, when performing sleep measurements at home using wearable and non-wearable sensors. Specifically, whether 3-14 days of data collection sufficient to capture an individual’s sleep habits and fluctuations in sleep patterns in a reliable way for research purposes. Our secondary goals are to investigate whether there is a relationship between sleep quality, physical activity, and heart rate, and whether individuals who exhibit similar activity and sleep patterns in general and in relation to seasonality can be clustered together. METHODS Data on sleep, physical activity, and heart rate was collected over a period of 6 months from 54 individuals in Denmark aged 52-86 years. The Withings Aura sleep tracker (non-wearable) and Withings Steel HR smartwatch (wearable) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. RESULTS Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We show specifically that in order to get more robust individual assessment of sleep and physical activity patterns through wearable and non-wearable devices, a longer evaluation period than 3-14 days is necessary. Additionally, we found seasonal patterns in sleep data related to changing of the clock for Daylight Saving Time (DST). CONCLUSIONS We demonstrate that over two months worth of self-tracking data is needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3-14 days for sleep quality assessment and call for rethinking standards when collecting data for research purposes. Seasonal patterns and DST clock change are also important aspects that need to be taken into consideration, and designed for, when choosing a period for collecting data. Furthermore, we suggest using consumer-grade self-trackers (wearable and non-wearable ones) to support longer term evaluations of sleep and physical activity for research purposes and, possibly, clinical ones in the future.

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 Extending UML-RT for Control System Modeling

2004 ◽  
Vol 1 (4) ◽  
pp. 338-347 ◽  
Author(s):  
Qimin Gao ◽  
L.J. Brown ◽  
L .F. Capretz
Keyword(s):  
Control System ◽  
System Modeling

High Speed Ring-Based Distributed Networked Control System for Real-Time Multivariable Applications

2004 ◽  
Author(s):  
Sunghoon Kim ◽  
H. Kazerooni
Keyword(s):  
Control System ◽  
Network Architecture ◽  
High Speed ◽  
Networked Control System ◽  
Networked Control ◽  
Sensor Data ◽  
Network Systems ◽  
System A ◽  
Network Delays ◽  
High Control

A networked control system (NCS) is a control architecture where sensors, actuators and controllers are distributed and interconnected. It is advantageous in terms of interoperability, expandability, installation, volume of wiring, maintenance, and cost-effectiveness. Many distributed network systems of various topologies and network type have been developed, but NCS systems tend to suffer from such issues as nondeterminism, long network delays, large overheads and unfairness. This paper presents the ring-based protocol, called the ExoNet, and its network architecture which are built to achieve better performance as a distributed networked system. A Cypress transceiver CY7C924ADX is applied to the network as a communication unit. The protocol is based on the transceiver and developed to achieve fast communication and allowable latency for controls with high control loop frequency. Compared with other standard network types such as Ethernet, ControlNet or DeviceNet, the network is characterized by its ring-based architecture, simple message and packet formats, one-shot distribution of control data and collection of sensor data, multi-node transmission, echo of a message, and other features. The network also guarantees determinism, collision-free transmission, relatively small overhead, fairness between nodes and flexibility in configuration. Its analysis and comparison with these network types are also provided and its application on the Berkeley Lower-Extremity Exoskeleton (BLEEX) is described.

Sign in / Sign up

Export Citation Format

Share Document