scholarly journals Integrating an MQTT Proxy in a LoRa-Based Messaging System for Generic Sensor Data Collection

2020 ◽  
pp. 282-294
Author(s):  
Kiyoshy Nakamura ◽  
Pietro Manzoni ◽  
Marco Zennaro ◽  
Juan-Carlos Cano ◽  
Carlos T. Calafate
Keyword(s):  
Data Collection ◽  
Sensor Data

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 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 Sensor Data Collection and Its Architecture with Internet of Things

2019 ◽  
Vol 9 (1) ◽  
pp. 2056-2060
Keyword(s):  
Data Collection ◽  
Web Service ◽  
Sensor Data ◽  
Cloud Platform ◽  
Lighting Condition ◽  
Machine Learning Methods ◽  
System Components ◽  
Sensor Information ◽  
Screen Temperature ◽  
Business Structure

Sensors are gadgets, which can screen temperature, moistness, weight, commotion levels, setting mindfulness, lighting condition and identify speed, position, and size of an Object. Sensor information are getting accumulated in gigantic amount thus they are overseen utilizing NOSQL. The information will be gathered in an IOT cloud stage where it will be additionally prepared with machine learning methods for prescient examination. What's more, eventually with the required answer for the business structure will be created. This paper explain the proposed system for IoT data collection with AWS (Amazon Web Service) cloud platform. Various system components like Kinesis stream, M2M platform, Notification service and secured IoT service layout. The complete BMS system architecture is detailed in this paper.

scholarly journals Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study (Preprint)

2019 ◽  
Author(s):  
Anna L Beukenhorst ◽  
Kelly Howells ◽  
Louise Cook ◽  
John McBeth ◽  
Terence W O'Neill ◽  
...  
Keyword(s):  
Data Collection ◽  
Knee Osteoarthritis ◽  
Patient Reported Outcomes ◽  
Health Data ◽  
Self Report ◽  
Sensor Data ◽  
Battery Life ◽  
Activity Data ◽  
Technical Problems ◽  
Patient Reported

BACKGROUND Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. OBJECTIVE This study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA). METHODS For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. RESULTS A total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day. CONCLUSIONS This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period. INTERNATIONAL REGISTERED REPORT RR2-10.2196/10238

Fast tag identification for mobile RFID robots in manufacturing environments

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Honggang Wang ◽  
Ruixue Yu ◽  
Ruoyu Pan ◽  
Mengyuan Liu ◽  
Qiongdan Huang ◽  
...  
Keyword(s):  
Data Collection ◽  
Sensor Data ◽  
System Throughput ◽  
Channel Activity ◽  
Activity Detection ◽  
Robot System ◽  
Content Type ◽  
Active Rfid ◽  
Manufacturing Environments ◽  
Uhf Band

Purpose In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection. Design/methodology/approach A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection. Findings The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission. Originality/value In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

Adaptive Flow Control Using Movement Information in Mobile-Assisted Sensor Data Collection

2020 ◽  
Vol 20 (20) ◽  
pp. 12435-12446
Author(s):  
Cheonyong Kim ◽  
Sangdae Kim ◽  
Kwansoo Jung
Keyword(s):  
Data Collection ◽  
Flow Control ◽  
Sensor Data ◽  
Adaptive Flow Control

scholarly journals Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 879 ◽  
Author(s):  
Uwe Köckemann ◽  
Marjan Alirezaie ◽  
Jennifer Renoux ◽  
Nicolas Tsiftes ◽  
Mobyen Uddin Ahmed ◽  
...  
Keyword(s):  
Data Collection ◽  
Activity Recognition ◽  
Care Home ◽  
Open Data ◽  
Ground Truth ◽  
Smart Homes ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Home Setting

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

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