Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities (2001.07416v2)

Abstract: The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions.

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The paper "Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities" provides an extensive examination of contemporary deep learning methodologies applied to sensor-based human activity recognition (HAR). This research aligns with the current demand for sophisticated monitoring systems in domains such as healthcare, smart environments, and user behavior analytics.

Core Challenges in HAR

The paper delineates several technical challenges integral to HAR:

1. Feature Extraction: Effective feature representation is crucial. Due to inter-activity similarities, distinguishing features are difficult to generate. The potential of deep learning lies in the automatic extraction of salient features from sensor data.
2. Annotation Scarcity: The acquisition and annotation of a comprehensive dataset is resource-intensive. The paper reviews unsupervised and semi-supervised learning models aimed at reducing dependence on extensive labeled data.
3. Class Imbalance: Imbalanced datasets can bias recognition systems toward prevalent classes. Strategies discussed include data augmentation and algorithmic adjustments like alternate loss functions.
4. Distribution Discrepancy: Variability in user behavior, time, and sensor configurations poses a challenge. The paper covers methodologies such as transfer learning to enhance model robustness across diverse conditions.
5. Complex Activities: Recognizing composite and concurrent activities requires hierarchical models capable of understanding sequences of atomic actions or multiple activities simultaneously.
6. Data Segmentation: Precise partitioning of sensor data streams is necessary to ensure consistent labeling within segments. Solutions involve dynamic segmentation techniques or enhanced learning models.
7. Computation Cost: Deployment on resource-constrained devices calls for optimization of deep learning models to maintain balance between performance and efficiency.
8. Privacy Concerns: Sensor data poses privacy risks, necessitating techniques that obscure identifiable information while retaining the intrinsic value for recognition tasks.
9. Model Interpretability: Understanding model decision-making processes is crucial for enhancement of usability and trust. Attention mechanisms and visualization of learned features are explored.

Implications and Opportunities

Practical implications are broad, significantly benefiting sectors engaged with pervasive sensing and real-time analytics. The theoretical insights call for focused algorithm development that aligns with emerging hardware capabilities.

Future developments could explore:

• Advancements in unsupervised and reinforcement learning for autonomous adaptation to evolving tasks.
• Innovative privacy-preserving techniques ensuring minimal compromise on recognition accuracy.
• Robust domain adaptation methods exploiting minimal annotated data for on-the-fly learning.

In conclusion, the paper rigorously surveys state-of-the-art methods while identifying persistent challenges and prospective research trajectories in the field of sensor-based human activity recognition. This work serves as a foundational reference for practitioners aiming to enhance or innovate HAR systems leveraging deep learning methodologies.