In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

There has been an extensive amount of study on cough detection using acoustic features captured from smartphones and smartwatches in the past decade. However, the specificity of the algorithms has always been a concern when exposed to the unseen field data containing cough-like sounds. In this paper, we propose a novel sensor fusion algorithm that employs a hybrid of classification and template matching algorithms to tackle the problem of unseen classes. The algorithm utilizes in-ear audio signal as well as head motion captured by the inertial measurement unit (IMU). A clinical study including 45 subjects from healthy and chronic cough cohorts was conducted that contained various tasks including cough and cough-like body sounds in various conditions such as quiet/noisy and stationary/non-stationary. Our hybrid model was evaluated for sensitivity and specificity in these conditions using leave one-subject out validation (LOSOV) and achieved an average sensitivity of 83% for stationary tasks and an specificity of 91.7% for cough-like sounds reducing the false positive rate by 55%. These results indicate the feasibility and superiority of fusion in earbuds platforms for detection of cough events.

Sign language builds up an important bridge between the d/Deaf and hard-of-hearing (DHH) and hearing people. Regrettably, most hearing people face challenges in comprehending sign language, necessitating sign language translation. However, state-of-the-art wearable-based techniques mainly concentrate on recognizing manual markers (e.g., hand gestures), while frequently overlooking non-manual markers, such as negative head shaking, question markers, and mouthing. This oversight results in the loss of substantial grammatical and semantic information in sign language. To address this limitation, we introduce SmartASL, a novel proof-of-concept system that can 1) recognize both manual and non-manual markers simultaneously using a combination of earbuds and a wrist-worn IMU, and 2) translate the recognized American Sign Language (ASL) glosses into spoken language. Our experiments demonstrate the SmartASL system's significant potential to accurately recognize the manual and non-manual markers in ASL, effectively bridging the communication gaps between ASL signers and hearing people using commercially available devices.

Automated detection of intake gestures with wearable sensors has been a critical area of research for advancing our understanding and ability to intervene in people's eating behavior. Numerous algorithms have been developed and evaluated in terms of accuracy. However, ensuring the system is not only accurate in making predictions but also efficient in doing so is critical for real-world deployment. Despite the growing research on accurate detection of intake gestures using wearables, many of these algorithms are often energy inefficient, impeding on-device deployment for continuous and real-time monitoring of diet. This paper presents a template-based optimized multicenter classifier that enables accurate intake gesture detection while maintaining low-inference time and energy consumption using a wrist-worn accelerometer and gyroscope. We designed an Intake Gesture Counter smartphone application (CountING) and validated the practicality of our algorithm against seven state-of-the-art approaches on three public datasets (In-lab FIC, Clemson, and OREBA). Compared with other methods, we achieved optimal accuracy (81.60% F1 score) and very low inference time (15.97 msec per 2.20-sec data sample) on the Clemson dataset, and among the top performing algorithms, we achieve comparable accuracy (83.0% F1 score compared with 85.6% in the top performing algorithm) but superior inference time (13.8x faster, 33.14 msec per 2.20-sec data sample) on the In-lab FIC dataset and comparable accuracy (83.40% F1 score compared with 88.10% in the top-performing algorithm) but superior inference time (33.9x faster, 16.71 msec inference time per 2.20-sec data sample) on the OREBA dataset. On average, our approach achieved a 25-hour battery lifetime (44% to 52% improvement over state-of-the-art approaches) when tested on a commercial smartwatch for continuous real-time detection. Our approach demonstrates an effective and efficient method, enabling real-time intake gesture detection using wrist-worn devices in longitudinal studies.

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.