scholarly journals Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2886 ◽  
Author(s):  
Judy Zhou ◽  
Sydney Y. Schaefer ◽  
Beth A. Smith
Keyword(s):  
Physical Activity ◽  
Case Report ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Future Research ◽  
Wearable Sensor ◽  
Sensor Signal ◽  
Background Motion ◽  
Full Day ◽  
Leg Movements

There is interest in using wearable sensors to measure infant movement patterns and physical activity, however, this approach is confounded by caregiver motion. The purpose of this study is to estimate the extent that caregiver motion confounds wearable sensor data in full-day studies of infant leg movements. We used wearable sensors to measure leg movements of a four-month-old infant across 8.5 hours, during which the infant was handled by the caregiver in a typical manner. A researcher mimicked the actions of the caregiver with a doll. We calculated 7744 left and 7107 right leg movements for the infant and 1013 left and 1115 right “leg movements” for the doll. In this case, approximately 15% of infant leg movements can be attributed to background motion of the caregiver. This case report is the first step toward removing caregiver-produced background motion from the infant wearable sensor signal. We have estimated the size of the effect and described the activities that were related to noise in the signal. Future research can characterize the noise in detail and systematically explore different methods to remove it.

scholarly journals Kinematic characteristics of infant leg movements produced across a full day

2017 ◽  
Vol 4 ◽  
pp. 205566831771746 ◽  
Author(s):  
Ivan A Trujillo-Priego ◽  
Beth A Smith
Keyword(s):  
Natural Environment ◽  
Sensor Data ◽  
Movement Behavior ◽  
Typical Development ◽  
Wearable Sensor ◽  
Kinematic Characteristics ◽  
Leg Movement ◽  
Kinematic Measures ◽  
Full Day ◽  
Leg Movements

Introduction Our purpose is to directly measure variability in infant leg movement behavior in the natural environment across a full day. We recently created an algorithm to identify an infant-produced leg movement from full-day wearable sensor data from infants with typical development between one and 12 months of age. Here we report the kinematic characteristics of their leg movements produced across a full day. Methods Wearable sensor data were collected from 12 infants with typical development for 8–13 h/day. A wearable sensor was attached to each ankle and recorded triaxial accelerometer and gyroscope measurements at 20 Hz. We determined the duration, average acceleration, and peak acceleration of each leg movement and classified its type (unilateral, bilateral synchronous, bilateral asynchronous). Results There was a range of leg movement duration (0.23–0.33 s) and acceleration (average 1.59–3.88 m/s2, peak 3.10–8.83 m/s2) values produced by infants across visits. Infants predominantly produced unilateral and asynchronous bilateral movements. Our results collected across a full day are generally comparable to kinematic measures obtained by other measurement tools across short periods of time. Conclusion Our results describe variable full-day kinematics of leg movements across infancy in a natural environment. These data create a reference standard for the future comparison of infants at risk for developmental delay.

scholarly journals Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3710 ◽  
Author(s):  
Rahul Soangra ◽  
Vennila Krishnan
Keyword(s):  
Physical Activity ◽  
Health Care ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Physical Activities ◽  
Sensor Data ◽  
Time Interval ◽  
Wearable Sensor ◽  
Obesity Management ◽  
Sleep Behavior

Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data to explore activities of daily living (ADL) and sleep behavior. The purpose of this study was to explore new clinically relevant metrics using movement amplitude and frequency from longitudinal wearable sensor data in obese and non-obese young adults. We utilized wavelet analysis to determine movement frequencies on longitudinal multi-day wearable sensor data. In this study, we recruited 10 obese and 10 non-obese young subjects. We found that obese participants performed more low-frequency (0.1 Hz) movements and fewer movements of high frequency (1.1–1.4 Hz) compared to non-obese counterparts. Both obese and non-obese subjects were active during the 00:00–06:00 time interval. In addition, obesity affected sleep with significantly fewer transitions, and obese individuals showed low values of root mean square transition accelerations throughout the night. This study is critical for obesity management to prevent unhealthy weight gain by the recommendations of physical activity based on our results. Longitudinal multi-day monitoring using wearable sensors has great potential to be integrated into routine health care checkups to prevent obesity and promote physical activities.

Strategies to enhance compliance to physical activity for patients with insulin resistance

2007 ◽  
Vol 32 (3) ◽  
pp. 549-556 ◽  
Author(s):  
Alison Kirk ◽  
Pierpaolo De Feo
Keyword(s):  
Physical Activity ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Wearable Sensors ◽  
Physical Activity Promotion ◽  
Physical Activity Behaviour ◽  
Future Research ◽  
Evidence Based ◽  
Activity Promotion

The evidence that physical activity is an effective therapeutic tool in the management of insulin resistance and type 2 diabetes is well documented. Limited research has addressed how best to promote and maintain physical activity in these individuals. This paper explores strategies to enhance compliance to physical activity for patients with insulin resistance. Several evidence-based guidelines and reviews recommend that physical activity interventions are based on a valid theoretical framework. However, there is no evidence-based consensus on the best theory or the combination of theories to use. Motivational tools such as pedometers, wearable sensors measuring energy expenditure, and point of choice prompts appear to be effective at stimulating short-term substantial increases in physical activity, but further strategies to maintain physical activity behaviour change are required. Physical activity consultation has demonstrated effective physical activity promotion over periods of up to 2 years in people with type 2 diabetes. Future research should identify the longer term effects of this intervention and the effectiveness of different methods of delivery. Overall, there needs to be a lot more focus on this area of research. Without this, the abundance of research investigating the effects of physical activity on people with insulin resistance and type 2 diabetes is essentially redundant.

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 Automated P-Wave Quality Assessment for Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Diogo Tecelão ◽  
Peter Charlton
Keyword(s):  
At Risk ◽  
Quality Assessment ◽  
Wearable Sensors ◽  
P Wave ◽  
Sensor Data ◽  
Wearable Sensor ◽  
High Quality ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Morphology

Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), an arrhythmia which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. However, ECG signals acquired by wearable sensors are susceptible to artefact, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high-quality P-waves, for AF prediction. We designed a two-stage algorithm which uses P-wave template-matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high-quality P-waves with high sensitivity (93%) and good specificity (82%), indicating that it may have utility for identifying high-quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high-quality P-waves could be used to predict AF, improving patient outcomes, and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation.

scholarly journals What Does Big Data Mean for Wearable Sensor Systems?

2014 ◽  
Vol 23 (01) ◽  
pp. 135-142 ◽  
Author(s):  
N. H. Lovell ◽  
G. Z. Yang ◽  
A. Horsch ◽  
P. Lukowicz ◽  
L. Murrugarra ◽  
...  
Keyword(s):  
Big Data ◽  
Health Management ◽  
Wearable Sensors ◽  
Scientific Progress ◽  
Sensor Data ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Negative Consequences ◽  
Inference Problems ◽  
Care Working Group

Summary Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Health-care Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

scholarly journals Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study

2018 ◽  
Vol 2 ◽  
pp. 17
Author(s):  
Joanne Shida-Tokeshi ◽  
Christianne J. Lane ◽  
Ivan A. Trujillo-Priego ◽  
Weiyang Deng ◽  
Douglas L. Vanderbilt ◽  
...  
Keyword(s):  
Natural Environment ◽  
Arm Movement ◽  
Sensor Data ◽  
Typical Development ◽  
Wearable Sensor ◽  
Developmental Status ◽  
Area Of Interest ◽  
Kinematic Characteristics ◽  
Movement Characteristics ◽  
Full Day

Background: Advances in wearable sensor technology now allow us to quantify the number, type and kinematic characteristics of bouts of infant arm movement made across a full day in the natural environment. Our aim here was to determine whether the amount and kinematic characteristics of arm movements made across the day in the natural environment were related to developmental status in infants with typical development as they learned to reach for objects using their arms. Methods: We used wearable sensors to measure arm movement across days and months as infants developed arm reaching skills. In total, 22 infants with typical development participated, aged between 38 and 203 days. Of the participants, 2 infants were measured once and the other 20 infants were measured once per month for 3 to 6 visits. The Bayley Scales of Infant Development was used to measure developmental level. Results: Our main findings were: 1) infant arm movement characteristics as measured by full-day wearable sensor data were related to Bayley motor, cognitive and language scores, indicating a relationship between daily movement characteristics and developmental status; 2) infants who moved more had larger increases in language and cognitive scores across visits; and 3) larger changes in movement characteristics across visits were related to higher motor scores. Conclusions: This was a preliminary, exploratory, small study of the potential importance of infant arm movement characteristics as measured by full-day wearable sensor data. Our results support full-day arm movement activity as an area of interest for future study as a biomarker of neurodevelopmental status and as a target for early intervention.

scholarly journals Wearable Sensor Application for Integrated Early Warning and Health Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren E Charles ◽  
Devin P Wright ◽  
Zhuanyi Huang ◽  
Cree White ◽  
Fnu Anubhav ◽  
...  
Keyword(s):  
Health Status ◽  
Real Time ◽  
Early Warning ◽  
Wearable Sensors ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

scholarly journals Towards non-laboratory prediction of relative physical activity intensities from multimodal wearable sensor data

2017 ◽  
Author(s):  
Alok Kumar Chowdhury ◽  
Dian Tjondronegoro ◽  
Jinglan Zhang ◽  
Puspa Setia Pratiwi ◽  
Stewart G. Trost
Keyword(s):  
Physical Activity ◽  
Sensor Data ◽  
Wearable Sensor
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