Browsing by Subject "accelerometer"

Now showing 1 - 4 of 4
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    Examining adherence to activity monitoring devices to improve physical activity in adults with cardiovascular disease: A systematic review
    (Sage, 2019-03) Marin, Tania S.; Kourbelis, Constance; Foote, Jonathon; Newman, Peter; Brown, Alex; Daniel, Mark; Coffee, Neil T.; Nicholls, Stephen; Ganesan, Anand; Versace, Vincent L.; Beks, Hannah; Haedtke, Christine A.; Clark, Robyn A.; School of Nursing
    Background Activity monitoring devices are currently being used to facilitate and monitor physical activity. No prior review has examined adherence to the use of activity monitoring devices amongst adults with cardiovascular disease. Methods Literature from June 2012 to October 2017 was evaluated to examine the extent of adherence to any activity monitoring device used to collect objective physical activity data. Randomized control trials comparing usual care against the use of an activity monitoring device, in a community intervention for adults from any cardiovascular diagnostic group, were included. A systematic search of databases and clinical trials registers was conducted using Joanna Briggs Institute methodology. Results Of 10 eligible studies, two studies reported pedometer use and eight accelerometer use. Six studies addressed the primary outcome. Mean adherence was 59.1% (range 39.6% to 85.7%) at last follow-up. Studies lacked equal representation by gender (28.6% female) and age (range 42 to 82 years). Conclusion This review indicates that current research on activity monitoring devices may be overstated due to the variability in adherence. Results showed that physical activity tracking in women and in young adults have been understudied.
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    Lower objectively measured physical activity is linked with perceived risk of hypoglycemia in type 1 diabetes
    (Elsevier, 2018) Keshawarz, Amena; Piropato, Andrew R.; Brown, Talia L.; Duca, Lindsey M.; Sippl, Rachel M.; Wadwa, R. Paul; Snell-Bergeon, Janet K.; Medicine, School of Medicine
    Aims Compare physical activity (PA) levels in adults with and without type 1 diabetes and identify diabetes-specific barriers to PA. Methods Forty-four individuals with type 1 diabetes and 77 non-diabetic controls in the Coronary Artery Calcification in Type 1 Diabetes study wore an accelerometer for 2 weeks. Moderate-to-vigorous physical activity (MVPA) was compared by diabetes status using multiple linear regression. The Barriers to Physical Activity in Type 1 Diabetes questionnaire measured diabetes-specific barriers to PA, and the Clarke hypoglycemia awareness questionnaire measured hypoglycemia frequency. Results Individuals with type 1 diabetes engaged in less MVPA, fewer bouts of MVPA, and spent less time in MVPA bouts per week than individuals without diabetes (all p < 0.05), despite no difference in self-reported PA (p > 0.05). The most common diabetes-specific barrier to PA was risk of hypoglycemia. Individuals with diabetes reporting barriers spent less time in MVPA bouts per week than those not reporting barriers (p = 0.047). Conclusions Individuals with type 1 diabetes engage in less MVPA than those without diabetes despite similar self-reported levels, with the main barrier being perceived risk of hypoglycemia. Adults with type 1 diabetes require guidance to meet current PA guidelines and reduce cardiovascular risk.
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    PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor
    (Elsevier, 2021-10) Mahajan, Yohan; Bhimireddy, Ananth; Abid, Areeba; Gichoya, Judy W.; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced.
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    Smart Unit Care for Pre Fall Detection and Prevention
    (IEEE, 2016-07) Thella, Ashok Kumar; Suryadevara, Vinay Kumar; Rizkalla, Maher; Hossain, Gahangir; Electrical and Computer Engineering, School of Engineering and Technology
    Generally falls may occur from moving or resting postures. This may include slipping from bed and fall from a sitting, or from running or walking. The pre-fall is a non-equilibrium state of human position that may lead to serious injuries, and may negatively impact the quality life condition, particularly for elders. Physical disabilities resulting from the fall incidences may lead to high costs involved with the healing process. In this work, an embedded sensor system using Arduino micro-controller was utilized to coordinate the data received from accelerometer and gyroscope. For a given threshold voltage and fall pattern, the fall decision is made by the microcontroller, citing an incoming fall. The study addresses the number of sensors to be coordinated for enhancing probability of receiving a real fall. Sensors are suggested to be placed on the human body within a belt, and safety devices at human body as well as incorporated in a smart room will be coordinated with the processor commands. Near 150 ms time frame was detected from the simulation results, suggesting a safety device to be triggered and activated for protection within this time frame. This paper discusses the research parameters such as response time for the device activation and interfacing the microcontroller to airbag switch, and means of activating the safety devices within the sharp edges in the smart unit care to minimize the impact of the fall injuries.