A Framework for Pervasive and Ubiquitous Geriatric Monitoring

Balaji, Ramesh; Bhavsar, Karan; Bhowmick, Brojeshwar; Mithun, B S; Chakravarty, Kingshuk; Chatterjee, Debatri; Ghose, Avik; Gupta, Puneet; Jaiswal, Dibyanshu; Kimbahune, Sanjay; Muralidharan, Kartik; Pal, Arpan; Sinha, Aniruddha; Venkatachari, Srinivasa Raghavan

doi:10.1007/978-3-319-92037-5_17

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…However, with the recent advances in gyroscope and battery technologies, these obstacles have been overcome for most settings. Further, in an effort to save energy, some studies used a hybrid approach where the gyroscope was only activated when the accelerometer detected a series of eating associated gestures [70]. A similar approach was used to start recordings with wearable cameras [7].…”

Section: Discussionmentioning

confidence: 99%

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

et al. 2019

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Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM, n = 21), Random Forest (n = 19), Decision Tree (n = 16), Hidden Markov Model (HMM, n = 10) algorithms, and from 2017 Deep Learning (n = 5). While comparisons of the detection models are not valid due to the use of different datasets, the models that consider the sequential context of data across time, such as HMM and Deep Learning, show promising results for eating activity detection. We discuss opportunities for future research and emerging applications in the context of dietary assessment and monitoring.

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Section: Discussionmentioning

confidence: 99%

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

et al. 2019

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“…There are several works [ 5 , 10 , 11 ] which analyze the physical activity of the user from a wrist-mounted wearable. Liu et al [ 5 ] proposed a model to distinguish usage patterns, energy consumption and network traffic to optimize battery consumption.…”

Section: Related Workmentioning

confidence: 99%

“…We believe it is therefore necessary to analyze the use of smartwatches in the real world, including different brands and the use of a diverse group of users to gain a holistic overview of their usage. Although promising initial research has been carried out in this field, further research is still required to investigate the significant challenges of this area [ 5 , 9 , 10 , 11 , 12 ]. In particular, research into human–computer interaction (HCI) is a challenge when research goes beyond the laboratory and is carried out in the real world [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding Smartwatch Battery Utilization in the Wild

Homayounfar

Malekijoo

Visuri

et al. 2020

Sensors

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Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

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