WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Abstract

This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.

Prediction and evaluation of healthy and unhealthy status of COVID-19 patients using wearable device prototype data

“…In the first phase, it uses the bootstrap sampling method to bootstrap the samples as f 1 (x), f 2 (x) ...F M (x) to obtain f(x) utilizing model averaging. The second phase defines the criteria in classifying the trees as daughter nodes and implements a simple vote [7] .…”

Soft Wearable Healthcare Materials and Devices

“…To achieve closed‐loop sensing and feedbacks, it is crucial to record and analyze the bio‐signals acquired by the wearable sensors to develop machine learning algorithms to diagnose or predict the physiology signatures of the wearers. [ 110,231 ] In this regard, three types of data analyzing algorithms are commonly applied, such are artificial neural networks (ANNs), convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks augmented recurrent neural network (RNNs). [ 110,232–237 ] For example, an ANN algorithm was trained to classify human subjects’ physiological states by extracting and analyzing collected EP signals by a set of skin‐friendly soft wearable sensors.…”

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

“…These methods may be executed in dedicated servers, smartphones, and even in low profile devices. Additionally, the combination of IoT, ML, and wearable devices requires efficient algorithms in order to reduce energy consumption [8].…”