Using Deep Learning for Energy Expenditure Estimation with wearable sensors

Zhu, Jindan; Pande, Amit; Mohapatra, Prasant; Han, Jay J.

doi:10.1109/healthcom.2015.7454554

Cited by 57 publications

(68 citation statements)

References 18 publications

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“…An example of wearable applications to predict the freezing of gait in Parkinson disease patients can be found in [169]. In [170], the authors try to predict energy expenditure (considered important in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases) from triaxial accelerometers and heart rate sensor data.…”

Section: Other Non-electrical Biomedical Datamentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Other Non-electrical Biomedical Datamentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…Additionally, the underlying relationship between variables was assumed to be non-linear. For such cases the literature supports [40][41][42][43][44][45][46][47] using gradient tree boosting and deep learning methods for better prediction results.…”

Section: Covariatesmentioning

confidence: 99%

Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic

Mehta

Julaiti

Griffin

et al. 2020

Preprint

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Importance:The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to rapidly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread.Objective: Developing county level prediction around near future disease movement for Main Outcome(s) and Measure(s):We developed a 3-stage model to quantify, firstly the probability of COVID-19 occurrence for unaffected counties using XGBoost classifier and secondly, the number of potential occurrences of a county via XGBoost regression. Thirdly, these results are combined to compute the county level risk. This risk is then used as an estimated after-five-day-vulnerability of the county. Results:Using data from March 14-31, 2020, the model shows a sensitivity over 71.5% and specificity over 94%. Conclusions and Relevance:We found that population, population density, percentage of people aged 70 or greater and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We found a positive association between affected and urban counties as well as less vulnerable and rural counties. The developed model can be used for

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“…Ordóñez and Roggen architect an advanced ConvLSTM to fuse data gathered from multiple sensors and perform activity recognition [112]. By leveraging CNN and LSTM structures, ConvLSTMs can automatically compress spatio-temporal sensor data into low-dimensional [236] Mobile ear Edge-based CNN Jindal [237] Heart rate prediction Cloud-based DBN Kim et al [238] Cytopathology classification Cloud-based CNN Sathyanarayana et al [239] Sleep quality prediction Cloud-based MLP, CNN, LSTM Li and Trocan [240] Health conditions analysis Cloud-based Stacked AE Hosseini et al [241] Epileptogenicity localisation Cloud-based CNN Stamate et al [242] Parkinson's symptoms management Cloud-based MLP Quisel et al [243] Mobile health data analysis Cloud-based CNN, RNN Khan et al [244] Respiration [250] Facial recognition Cloud-based CNN Wu et al [291] Mobile visual search Edge-based CNN Rao et al [251] Mobile augmented reality Edge-based CNN Ohara et al [290] WiFi-driven indoor change detection Cloud-based CNN,LSTM Zeng et al [252] Activity recognition Cloud-based CNN, RBM Almaslukh et al [253] Activity recognition Cloud-based AE Li et al [254] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [255] Smart watch-based activity recognition Edge-based RBM Antreas and Angelov [256] Mobile surveillance system Edge-based & Cloud based CNN Ordóñez and Roggen [112] Activity recognition Cloud-based ConvLSTM Wang et al [257] Gesture recognition Edge-based CNN, RNN Gao et al [258] Eating detection Cloud-based DBM, MLP Zhu et al [259] User energy expenditure estimation Cloud-based CNN, MLP Sundsøy et al [260] Individual income classification Cloud-based MLP Chen and Xue [261] Activity recognition Cloud-based CNN Ha and Choi [262] Activity recognition Cloud-based CNN Edel and Köppe [263] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al…”

Section: Mobilementioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,377

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Using Deep Learning for Energy Expenditure Estimation with wearable sensors

Cited by 57 publications

References 18 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic

Deep Learning in Mobile and Wireless Networking: A Survey

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