A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation

Angeles, Paolo; Mace, Michael; Admiraal, Marcel; Burdet, Etienne; Pavese, Nicola; Vaidyanathan, Ravi

doi:10.1007/978-3-319-40379-3_2

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…In healthcare, inertial sensor data can be used for monitoring the onset of diseases as well as the efficacy of treatment options [11], [12]. For patients with neurodegenerative diseases, such as Parkinson's, HAR can be used to compile diaries of their daily activities and detect episodes such as freezing-ofgait events, for assessing the patient's condition [13]. Quantifying physical activity through HAR can also provide invaluable information for other applications, such as evaluating the condition of patients with chronic obstructive pulmonary disease (COPD) [14], [15] or evaluating the recovery progress of patients during rehabilitation [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

Ravì

Wong

et al. 2017

IEEE J. Biomed. Health Inform.

381

204

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The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

Ravì

Wong

et al. 2017

IEEE J. Biomed. Health Inform.

381

204

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show abstract

“…To demonstrate on-node activity recognition, the proposed framework is implemented on Intel Edison. To implement deep learning model and extracting spectrogram we make use of FFTW3 library [9] and Torch Framework [10].…”

Section: Fig 8accuracy Values Of the Combined Approachmentioning

confidence: 99%

“…Models and algorithms such as AdaBoost, K-Nearest Neighbors (KNN), Baye's classifiers, Gaussian models, multilayer perceptrons, Support Vector Machines (SVM) and Markov models are implemented [10].…”

Section: Introduction To the Torch Frameworkmentioning

confidence: 99%

Deep Learning for Human Activity Recognition using on-Node Sensors

Pravallika*,

Sreenivasarao,

Saheb

2020

IJRTE

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Due to advancement in technology, availability of resources and by increased utilization of on node sensors enormous amount of data is obtained. There is a necessity of analyzing and classifying this physiological information by efficient and effective approaches such as deep learning and artificial intelligence. Human Activity Recognition (HAR) is assuming a dominant role in sports, security, anti-crime, healthcare and also in environmental applications like wildlife observation etc. Most techniques work well for processing offline instead of real- time processing. There are few approaches which provide maximum accuracy for real time processing of large-scale data, one of the compromising approaches is deep learning. Limitation of resources is one of the causes to restrict the usage of deep learning for low power devices which can be worn on our body. Deep learning implementations are known to produce precise results for different computing systems.We suggest a deep learning approach in this paper which integrates features and data learned from inertial sensors with complementary knowledge obtained from a collection of shallow features which generates the possibility of performing real time activity classification accurately. Eliminating the obstructions caused by using deep learning methods for real-time analysis is the aim of this integrated design. Before passing the data into the deep learning framework, we perform spectral analysis to optimize the planned methodology for on-node computation. The accuracy obtained by combined approach is tested by utilizing datasets obtained from laboratory and real world controlled and uncontrolled environment. Our outcomes demonstrate the legitimacy of the methodology on various human action datasets, beating different techniques, including the two strategies utilized inside our consolidated pipeline. We additionally exhibit that our integrated design's classification times are reliable with on node real-time analysis criteria on smart phones and wearable technology.

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“…In the previous work, the data are usually analysed by traditional technologies like linear regression or other statistical methods. For example, Angeles et al [116] use statistical algorithms to distinguish between non-mimicked and mimicked tests for all the Parkinson's primary symptoms, with very convincing differences. To evaluate the patients in rehabilitation recovery progress [117], Chen et al [118] use some validation techniques, such as 10-fold cross-validation.…”

Section: A Quantified Selfmentioning

confidence: 99%

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

et al. 2019

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Big medical data pose great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists, and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of the state-of-the-practice and state-of-the-art systems handling big medical data currently or in the future? Finally, but not least, are we, life scientists, clinicians, computer scientists, and engineers, ready for working together? We believe that answering the above-mentioned issues will help define and shape the landscape of big medical data.

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A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation

Cited by 7 publications

References 14 publications

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

Deep Learning for Human Activity Recognition using on-Node Sensors

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

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