This study presents a novel approach for golf swing biomechanics through deep learning-assisted to enhance the sensing, processing, interpretation, and assessment of swing quality. The proposed methodology introduces a smart golf glove (SGG) system, incorporating a deep neural network and the Internet of Things (IoT) capabilities. The key contributions of this study include 1) a comprehensive design framework encompassing both hardware and software aspects of the SGG system, as well as 2) an algorithm of phase and events to segment swing signals as inputs for deep learning models. Moreover, 3) an unsupervised learning model (ULM) architecture is introduced to address the challenges of dataset limitation and effort for data labeling. Additionally, 4) an IoT-assisted SGG platform is proposed, enabling remote monitoring and management. Experimental results indicate that the trained ULM model achieved an average accuracy of 92.4%. The findings highlight the effectiveness of the proposed SGG system in accurately detecting abnormal motion from beginner players. The integration of artificial intelligence (AI) and IoT-based platforms with targeted videos-based self-coaching capabilities represents a significant advancement in golf swing biomechanics analysis.

Dissemination of sensors and advances in techniques (e.g., network) has led to the opportunity for smart home. However, sensor malfunctions and difficult-to-diagnose characteristics hinder robust sensor system operation. Sensor anomaly detection systems for smart home have been proposed, but they target only a few specific types of sensor anomalies of a single sensor. In this work, we propose a sensor anomaly detection method based on Deep Neural Network (DNN), which automatically extracts critical features to detect the anomalies, even for simultaneous sporadic anomalies with complex data patterns. We leverage Hypersphere Classification (HSC) [14], the state-of-the-art DNN-based supervised outlier exposure method. We evaluate our proposed method on a public smart home sensor dataset. Our results show that the performances of the baselines drop up to 54.4% while ours drops up to 1.1%.

In this work, we show the severity of the thermal issue in mobile Extended Reality (XR) applications. We implement three XR applications and run the applications on a mobile device. We compare the device temperature running benchmark and XR applications. We find that long-term multi-DNN inference execution is the main cause of the thermal issue.

We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability.To tackle this problem, we propose a novel end-to-end deep network model for reading comprehension, which we refer to as Episodic Memory Reader (EMR) that sequentially reads the input contexts into an external memory, while replacing memories that are less important for answering unseen questions.Specifically, we train an RL agent to replace a memory entry when the memory is full, in order to maximize its QA accuracy at a future timepoint, while encoding the external memory using either the GRU or the Transformer architecture to learn representations that considers relative importance between the memory entries.We validate our model on a synthetic dataset (bAbI) as well as real-world large-scale textual QA (TriviaQA) and video QA (TVQA) datasets, on which it achieves significant improvements over rulebased memory scheduling policies or an RLbased baseline that independently learns the query-specific importance of each memory.

It has been a common approach to apply Machine Learning (ML) techniques over sensory data for inferring human behavior, activities, emotions, and surrounding contexts. Especially, IMU (Inertial Measurement Unit) sensors are widely used to obtain dataset to train ML models for human activity recognition. A key challenge in building highly accurate ML models lies in collecting a wide variety of activity data from a large number of users. Such data collection is often a highly time-consuming and costly process.