scholarly journals Action Sports Learning Based on Expert Instruction Revised by Wearable Sensor Data

2018 ◽  
Keyword(s):  
Sensor Data ◽  
Wearable Sensor ◽  
Action Sports

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

scholarly journals Direct Lightweight Temporal Compression for Wearable Sensor Data

2021 ◽  
Vol 5 (2) ◽  
pp. 1-4
Author(s):  
Lucie Klus ◽  
Roman Klus ◽  
Elena Simona Lohan ◽  
Carlos Granell ◽  
Jukka Talvitie ◽  
...  
Keyword(s):  
Sensor Data ◽  
Wearable Sensor ◽  
Temporal Compression

Estimating Clinical Scores From Wearable Sensor Data In Stroke Survivors

2017 ◽  
Vol 98 (10) ◽  
pp. e65
Author(s):  
Claire Meagher ◽  
Stefano Sapienza ◽  
Catherine Adans-Dester ◽  
Anne O’Brien ◽  
Shyamal Patel ◽  
...  
Keyword(s):  
Sensor Data ◽  
Stroke Survivors ◽  
Wearable Sensor ◽  
Clinical Scores

scholarly journals Semantic representation and processing of hypoglycemic events derived from wearable sensor data

2017 ◽  
Vol 9 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jean-Paul Calbimonte ◽  
Jean-Eudes Ranvier ◽  
Fabien Dubosson ◽  
Karl Aberer
Keyword(s):  
Semantic Representation ◽  
Sensor Data ◽  
Wearable Sensor
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