Cheating off your neighbors: Improving activity recognition through corroboration

Understanding the complexity of human activities solely through an individual's data can be challenging. However, in many situations, surrounding individuals are likely performing similar activities, while existing human activity recognition approaches focus almost exclusively on individual measurements and largely ignore the context of the activity. Consider two activities: attending a small group meeting and working at an office desk. From solely an individual's perspective, it can be difficult to differentiate between these activities as they may appear very similar, even though they are markedly different. Yet, by observing others nearby, it can be possible to distinguish between these activities. In this paper, we propose an approach to enhance the prediction accuracy of an individual's activities by incorporating insights from surrounding individuals. We have collected a real-world dataset from 20 participants with over 58 hours of data including activities such as attending lectures, having meetings, working in the office, and eating together. Compared to observing a single person in isolation, our proposed approach significantly improves accuracy. We regard this work as a first step in collaborative activity recognition, opening new possibilities for understanding human activity in group settings

An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition

Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the masked segments from extensive unlabeled data. In human activity recognition, this pretext task employed a masking strategy centered on the time dimension. However, this masking strategy fails to fully exploit the inherent characteristics of wearable sensor data and overlooks the inter-channel information coupling, thereby limiting its potential as a powerful pretext task. To address these limitations, we propose a novel masking strategy called Channel Masking. It involves masking the sensor data along the channel dimension, thereby compelling the encoder to extract channel-related features while performing the masked reconstruction task. Moreover, Channel Masking can be seamlessly integrated with masking strategies along the time dimension, thereby motivating the self-supervised model to undertake the masked reconstruction task in both the time and channel dimensions. Integrated masking strategies are named Time-Channel Masking and Span-Channel Masking. Finally, we optimize the reconstruction loss function to incorporate the reconstruction loss in both the time and channel dimensions. We evaluate proposed masking strategies on three public datasets, and experimental results show that the proposed strategies outperform prior strategies in both self-supervised and semi-supervised scenarios

Mobile sensor data anonymization

Data from motion sensors such as accelerometers and gyroscopes embedded in our devices can reveal secondary undesired, private information about our activities. This information can be used for malicious purposes such as user identification by application developers. To address this problem, we propose a data transformation mechanism that enables a device to share data for specific applications (e.g.~monitoring their daily activities) without revealing private user information (e.g.~ user identity). We formulate this anonymization process based on an information theoretic approach and propose a new multi-objective loss function for training convolutional auto-encoders~(CAEs) to provide a practical approximation to our anonymization problem. This effective loss function forces the transformed data to minimize the information about the user's identity, as well as the data distortion to preserve application-specific utility. Our training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the final output independently of users in the training set. Then, a trained CAE can be deployed on a user's mobile device to anonymize sensor data before sharing with an app, even for users who are not included in the training dataset. The results, on a dataset of 24 users for activity recognition, show a promising trade-off on transformed data between utility and privacy, with an accuracy for activity recognition over 92%, while reducing the chance of identifying a user to less than 7%

Embedding and learning with signatures

Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. The present article is concerned with a novel approach for sequential learning, called the signature method, and rooted in rough path theory. Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies critically on an embedding principle, which consists in representing discretely sampled data as paths, i.e., functions from [0,1] to R^d. After a survey of machine learning methodologies for signatures, we investigate the influence of embeddings on prediction accuracy with an in-depth study of three recent and challenging datasets. We show that a specific embedding, called lead-lag, is systematically better, whatever the dataset or algorithm used. Moreover, we emphasize through an empirical study that computing signatures over the whole path domain does not lead to a loss of local information. We conclude that, with a good embedding, the signature combined with a simple algorithm achieves results competitive with state-of-the-art, domain-specific approaches