scholarly journals Evaluation of Accelerometer-Derived Data in the Context of Cycling Cadence and Saddle Height Changes in Triathlon

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 871
Author(s):  
Stuart A. Evans ◽  
Daniel A. James ◽  
David Rowlands ◽  
James B. Lee
Keyword(s):  
Perceived Exertion ◽  
Sensor Data ◽  
Sensor Technology ◽  
Triaxial Accelerometer ◽  
Longitudinal Acceleration ◽  
Rate Of Perceived Exertion ◽  
Trunk Movements ◽  
Polynomial Expression ◽  
Reduced Rate ◽  
Derived Data

In the multisport of triathlon cycling is the longest of the three sequential disciplines. Triathlon bicycles differ from road bicycles with steeper seat tube angles with a change to saddle height altering the seat tube angle. This study evaluated the effectiveness of a tri axial accelerometer to determine acceleration magnitudes of the trunk in outdoor cycling in two saddle positions. Interpretation of data was evaluated based on cadence changes whilst triathletes cycled in an aerodynamic position in two saddle positions. The evaluation of accelerometer derived data within a characteristic overground setting suggests a significant reduction in mediolateral acceleration of the trunk, yielding a 25.1% decrease when saddle height was altered alongside reduced rate of perceived exertion (3.9%). Minimal differences were observed in anteroposterior and longitudinal acceleration. Evaluation of sensor data revealed a polynomial expression of the subtle changes between both saddle positions. This study shows that a triaxial accelerometer has capability to continuously measure acceleration magnitude of trunk movements during an in-the-field, varied cadence cycle protocol. Accessible and practical sensor technology could be relevant for postural considerations when exploring saddle position in dynamic settings.

scholarly journals Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2637
Author(s):  
Padraig Davidson ◽  
Peter Düking ◽  
Christoph Zinner ◽  
Billy Sperlich ◽  
Andreas Hotho
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Perceived Exertion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rating Of Perceived Exertion ◽  
Sensor Data ◽  
Rate Step ◽  
Derived Data ◽  
Trained Runners

The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE > 15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.

scholarly journals The Effect of Cleat Position on Running Using Acceleration-Derived Data in the Context of Triathlons

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5899
Author(s):  
Stuart A. Evans ◽  
Daniel A. James ◽  
David Rowlands ◽  
James B. Lee
Keyword(s):  
Effect Size ◽  
Perceived Exertion ◽  
Metatarsal Head ◽  
Triaxial Accelerometer ◽  
Ratings Of Perceived Exertion ◽  
Large Effect Size ◽  
Running Performance ◽  
First Metatarsal ◽  
Trunk Acceleration ◽  
Derived Data

Appropriate cycling cleat adjustment could improve triathlon performance in both cycling and running. Prior recommendations regarding cleat adjustment have comprised aligning the first metatarsal head above the pedal spindle or somewhat forward. However, contemporary research has questioned this approach in triathlons due to the need to run immediately after cycling. Subsequently, moving the pedal cleat posteriorly could be more appropriate. This study evaluated the effectiveness of a triaxial accelerometer to determine acceleration magnitudes of the trunk in outdoor cycling in two different bicycle cleat positions and the consequential impact on trunk acceleration during running. Seven recreational triathletes performed a 20 km cycle and a 5 km run using their own triathlon bicycle complete with aerodynamic bars and gearing. Interpretation of data was evaluated based on cadence changes whilst triathletes cycled in an aerodynamic position in two cleat positions immediately followed by a self-paced overground run. The evaluation of accelerometer-derived data within a characteristic overground setting suggests a significant increase in total trunk acceleration magnitude during cycling with a posterior cleat with significant increases to longitudinal acceleration (p = 0.04) despite a small effect (d = 0.2) to the ratings of perceived exertion (RPE). Cycling with a posterior cleat significantly reduced longitudinal trunk acceleration in running and overall acceleration magnitudes (p < 0.0001) with a large effect size (d = 0.9) and a significant reduction in RPE (p = 0.02). In addition, running after cycling in a posterior cleat was faster compared to running after cycling in a standard cleat location. Practically, the magnitude of trunk acceleration during cycling in a posterior cleat position as well as running after posterior cleat cycling differed from that when cycling in the fore-aft position followed by running. Therefore, the notion that running varies after cycling is not merely an individual athlete’s perception, but a valid observation that can be modified when cleat position is altered. Training specifically with a posterior cleat in cycling might improve running performance when trunk accelerations are analysed.

DIFFERENCES IN BLOOD PRESSURE AND RATE OF PERCEIVED EXERTION RESPONSES IN ARM AND LEG EXERCISE.

2004 ◽  
Vol 18 (4) ◽  
pp. 30-31
Author(s):  
Konrad J. Dias ◽  
Kathleen Amos ◽  
Jennifer Koons ◽  
Patrick Martchink ◽  
Jared Smiddy ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Perceived Exertion ◽  
Rate Of Perceived Exertion ◽  
Leg Exercise

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

Validation of Wearable Sensor Technology to Measure Social Proximity of Young Children With Autism Spectrum Disorders

2021 ◽  
pp. 108835762110282
Author(s):  
Sarah N. Douglas ◽  
Yan Shi ◽  
Saptarshi Das ◽  
Subir Biswas
Keyword(s):  
Autism Spectrum Disorders ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Sensor System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Future Research ◽  
Social Proximity ◽  
Children With Asd ◽  
Spectrum Disorders

Children with autism spectrum disorders (ASD) struggle to develop appropriate social skills, which can lead to later social rejection, isolation, and mental health concerns. Educators play an important role in supporting and monitoring social skill development for children with ASD, but the tools used by educators are often tedious, lack suitable sensitivity, provide limited information to plan interventions, and are time-consuming. Therefore, we conducted a study to evaluate the use of a sensor system to measure social proximity between three children with ASD and their peers in an inclusive preschool setting. We compared video-coded data with sensor data using point-by-point agreement to measure the accuracy of the sensor system. Results suggest that the sensor system can adequately measure social proximity between children with ASD and their peers. The next steps for sensor system validation are discussed along with clinical and educational implications, limitations, and future research directions.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7919
Author(s):  
Sjoerd van Ratingen ◽  
Jan Vonk ◽  
Christa Blokhuis ◽  
Joost Wesseling ◽  
Erik Tielemans ◽  
...  
Keyword(s):  
Low Cost ◽  
Added Value ◽  
Sensor Data ◽  
Sensor Technology ◽  
Monitoring Networks ◽  
Technical Problems ◽  
Eu Directive ◽  
Short Period ◽  
No2 Sensor ◽  
One Year

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

Sensing Home: Designing an Open Tool That Lets People Collect and Interpret Simple Sensor Data from Their Homes

i-com ◽  
2018 ◽  
Vol 17 (2) ◽  
pp. 153-167
Author(s):  
Arne Berger ◽  
Albrecht Kurze ◽  
Sören Totzauer ◽  
Michael Storz ◽  
Kevin Lefeuvre ◽  
...  
Keyword(s):  
Design Space ◽  
Group Discussion ◽  
Design Tool ◽  
Sensor Data ◽  
Sensor Technology ◽  
The Internet ◽  
Smart Sensor ◽  
Design Rationale ◽  
Domestic Products ◽  
The Internet Of Things

AbstractThe Internet of Things in the home is a design space with huge potential. With sensors getting smaller and cheaper, smart sensor equipped objects will become an integral, preinstalled part of the future home. With this article we will reflect on Sensing Home, a design tool to explore sensors in the home together with people. Sensing Home allows people to integrate sensors and connectivity into mundane domestic products in order to make them smart. As such, it can be used by people to experience and explore sensors in the home and daily life. They may explore possible use cases, appropriate sensor technology, and learn about this technology through use. At the same time people may also be empowered to understand the issues and implications of sensors in the home. We present the design rationale of Sensing Home, five usage examples of how Sensing Home allowed people to explore sensor technology, and the deployment of Sensing Home together with a self-developed group discussion method to empower people to understand the benefits and pitfalls of sensors in their home. The article ends with a brief reflection whether Sensing Home is a probe or a toolkit.

Temporal Location of High-Intensity Interval Training in Cycling Does Not Impact the Time Spent Near Maximal Oxygen Consumption

2020 ◽  
pp. 1-6
Author(s):  
James R. Mckee ◽  
Bradley A. Wall ◽  
Jeremiah J. Peiffer
Keyword(s):  
Oxygen Consumption ◽  
Effect Size ◽  
Perceived Exertion ◽  
Minute Ventilation ◽  
Maximal Oxygen Consumption ◽  
Interval Training ◽  
High Intensity Interval Training ◽  
Rate Of Perceived Exertion ◽  
Temporal Location ◽  
High Intensity Interval

Purpose: To examine the influence of temporal location of high-intensity interval training (HIIT) within a cycling session on the time spent ≥90% of maximal oxygen consumption and physiological and perceptual responses. Methods: In a randomized, crossover design, 16 trained cyclists (male, n = 13 and female, n = 3) completed three 90-minute cycling sessions with HIIT placed at the beginning, middle, or end of the session (13, 36, and 69 min, respectively). Intervals consisted of three 3-minute efforts at 90% of the power output associated with maximal oxygen consumption interspersed with 3 minutes of recovery. Oxygen consumption, minute ventilation, respiratory rate, and heart rate were recorded continuously during work intervals. Rate of perceived exertion was recorded at the end of work intervals, and sessional rate of perceived exertion was collected 20 minutes after session completion. Results: No differences were observed for mean oxygen consumption (P = .479) or time spent ≥90% maximal oxygen consumption (P = .753) between condition. The mean rate of perceived exertion of all intervals were greater in the Middle (P < .01, effect size = 0.83) and End (P < .05, effect size = 0.75) compared with Beginning conditions. Mean minute ventilation was greater in the End compared with Beginning condition (P = .015, effect size = 0.63). However, no differences in mean respiratory rate were observed between conditions (P = .297). Conclusions: Temporal location of HIIT has no impact on oxygen consumption or cardiovascular stress within a cycling session. However, HIIT performed later in the session resulted in higher ventilation, which may indicate the need for greater anaerobic contribution to these intervals.

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