Understanding Smartwatch Battery Utilization in the Wild

Homayounfar, Morteza; Malekijoo, Amirhossein; Visuri, Aku; Dobbins, Chelsea; Peltonen, Ella; Pinsky, Eugene; Teymourian, Kia; Rawassizadeh, Reza

doi:10.3390/s20133784

Cited by 9 publications

(5 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For professional medical use, it is used to detect tremor caused by Parkinson’s disease [ 13 ] and classify complex behaviors that occur simultaneously [ 14 ]. Additionally, since wearable systems rely on the battery, tasks that affect battery life are classified using deep learning [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Jun

Choi

2020

Sensors

View full text Add to dashboard Cite

Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.

show abstract

Section: Introductionmentioning

confidence: 99%

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Jun

Choi

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…[14][15] [29] -Battery Life: Continuous data collection can drain the device's battery quickly, limiting the duration of monitoring sessions. [21] [30] -Data Interoperability: Ensuring compatibility and interoperability between different devices and platforms is crucial for aggregating and analyzing data from multiple sources seamlessly.…”

Section: Types Of Sensorsmentioning

confidence: 99%

Smartwatch-Assisted Exercise Prescription: Utilizing Machine Learning Algorithms for Personalized Workout Recommendations and Monitoring: A review

Jubair,

Mehenaz

2024

Preprint

View full text Add to dashboard Cite

This review paper examines the intersection of wearable technology, machine learning algorithms, and exercise prescription, focusing on the utilization of smartwatches to monitor physiological data during workouts. With the proliferation of smartwatches equipped with sensors capable of capturing various biometric parameters, alongside the advancements in machine learning, personalized exercise recommendations have become increasingly feasible. Through a synthesis of existing literature and analysis of recent developments, this paper explores the potential of integrating wearable technology and artificial intelligence to optimize exercise routines tailored to individual needs and goals. Key topics covered include the types of sensors found in smartwatches, machine learning algorithms used for exercise prescription, practical applications, challenges, and future directions. By providing insights into the current landscape and emerging trends, this review aims to inform researchers, practitioners, and policymakers on the opportunities and challenges in leveraging wearable technology and machine learning for personalized fitness monitoring and exercise prescription.

show abstract

“…Mobile and wearable devices are known for their limited battery capacity [29,58] and energy utilization directly affects the usability of the device [69]. We report the energy usage for query execution to identify the energy impact of ODSearch.…”

Section: Energy Usagementioning

confidence: 99%

ODSearch: A Fast and Resource Efficient On-device Information Retrieval for Mobile and Wearable Devices

Rong¹,

Rawassizadeh²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch, an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 55 times in execution time 1 , 26 times in energy usage, and 2.3% in memory utilization.

show abstract

Understanding Smartwatch Battery Utilization in the Wild

Cited by 9 publications

References 58 publications

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Smartwatch-Assisted Exercise Prescription: Utilizing Machine Learning Algorithms for Personalized Workout Recommendations and Monitoring: A review

ODSearch: A Fast and Resource Efficient On-device Information Retrieval for Mobile and Wearable Devices

Contact Info

Product

Resources

About