Wearable Human Action Recognition (wHAR) uses motion sensor data to identify human movements, which is essential for mobile and wearable devices. However, traditional wHAR systems are only trained on a limited set of activities. Hence, they fail to generalize to diverse human motions, prompting Zero-Shot Learning (ZSL). Existing ZSL methods for wHAR focus solely on augmenting labels, such as representing them as attribute matrices, images, videos, or text. We propose ZeroHAR that enhances ZSL by not just focusing on activity labels, but by augmenting motion data with sensor context features. Our approach incorporates information about the sensor type, the Cartesian axis of the data, and the sensor's body position, providing the model with crucial spatial and biomechanical insights. This helps the model generalize better to new actions. First, we train the model by aligning the latent space of the motion time-series with its corresponding sensor context, while distancing it from unrelated sensor contexts. Finally, we train the model using the target activity descriptions. We tested our method against eight baselines on five benchmark HAR datasets with various sensors, placements, and activities. Our model shows exceptional generalizability across 18 motion time series classification benchmark datasets, outperforming the best baselines by 262% in the zero-shot setting.

A supply chain is the network of entities and processes involved in the production and distribution of a commodity. Supply chains are a critical backbone across industries like retail, manufacturing, healthcare, and automotive, driving everything from product availability to operational efficiency and customer satisfaction. Modern supply chains are 1) non-cooperative, functioning as fragmented systems where isolated technologies solve individual problems without integration, and 2) unadaptable, failing to adjust to real-time data and uncertainties, like demand fluctuations and regulatory changes. As a result, frequent manual overrides are required, as even small errors can lead to significant financial losses, strained customer relationships, or reputational damage. Modern Artificial Intelligence (AI) advancements offer great potential to unify fragmented supply chains into a seamless, adaptive system while enhancing automation, decision-making, and transparency. In this workshop, we will examine critical supply chain challenges and demonstrate how AI can provide accurate, efficient, and scalable solutions.

Motion time series collected from low-power, always-on mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR. However, given security and privacy concerns, building large-scale motion time series datasets remains difficult, hindering the development of pre-trained models for human activity analysis. Typically, existing models are trained and tested on the same dataset, leading to poor generalizability across variations in device location, device mounting orientation, and human activity type. In this paper, we introduce UniMTS, the first unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities. Specifically, we employ a contrastive learning framework that aligns motion time series with text descriptions enriched by large language models. This helps the model learn the semantics of time series to generalize across activities. Given the absence of large-scale motion time series data, we derive and synthesize time series from existing motion skeleton data with all-joint coverage. We use spatio-temporal graph networks to capture the relationships across joints for generalization across different device locations. We further design rotation-invariant augmentation to make the model agnostic to changes in device mounting orientations. Our model shows exceptional generalizability across 18 motion time series classification benchmark datasets, outperforming the best baselines by 340% in the zero-shot setting, 16.3% in the few-shot setting, and 9.2% in the full-shot setting.

Real-world applications often involve irregular time series, for which the time intervals between successive observations are non-uniform. Irregularity across multiple features in a multi-variate time series further results in a different subset of features at any given time (i.e., asynchronicity). Existing pre-training schemes for time-series, however, often assume regularity of time series and make no special treatment of irregularity. We argue that such irregularity offers insight about domain property of the data—for example, frequency of hospital visits may signal patient health condition—that can guide representation learning. In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time-series. Specifically, we design a time sensitive contrastive learning and data reconstruction task to pre-train a model. Irregular time-series exhibits considerable variations in sampling density over time. Hence, our triplet generation strategy follows the density of the original data points, preserving its native irregularity. Moreover, the sampling density variation over time makes data reconstruction difficult for different regions. Therefore, we design a data masking technique that always masks a constant time duration to accommodate reconstruction for regions of different sampling density. We learn with these tasks using unlabeled data to build a pre-trained model and fine-tune on a downstream task with limited labeled data, in contrast with existing fully supervised approach for irregular time-series, requiring large amounts of labeled data. Experiment results show that PrimeNet significantly outperforms state-of-the-art methods on naturally irregular and asynchronous data from Healthcare and IoT applications for several downstream tasks, including classification, interpolation, and regression.

Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and "open fridge" both have "open" as the action; "kicking soccer ball" and "playing tennis ball" both have "ball" as the object. Such shared structures in label names can be translated to the similarity in sensory data and modeling common structures would help uncover knowledge across different activities, especially for activities with limited samples. In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities, including a basic token-level augmentation, and two enhanced embedding-level and sequence-level augmentations utilizing the capabilities of pre-trained models. SHARE outperforms state-of-the-art HAR models in extensive experiments on seven HAR benchmark datasets. We also evaluate in few-shot learning and label imbalance settings and observe even more significant performance gap.

Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification, where class labels can be preserved even if augmentation alters the temporal dynamics. We note that augmentation designed for forecasting requires diversity as well as coherence with the original temporal dynamics. As time-series data generated by real-life physical processes exhibit characteristics in both the time and frequency domains, we propose to combine Spectral and Time Augmentation (STAug) for generating more diverse and coherent samples. Specifically, in the frequency domain, we use the Empirical Mode Decomposition to decompose a time series and reassemble the subcomponents with random weights. This way, we generate diverse samples while being coherent with the original temporal relationships as they contain the same set of base components. In the time domain, we adapt a mix-up strategy that generates diverse as well as linearly in-between coherent samples. Experiments on five real-world time-series datasets demonstrate that STAug outperforms the base models without data augmentation as well as state-of-the-art augmentation methods.

Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects the performance and reliability of the systems they support. Classic filtering approaches introduce strong assumption on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohibitive to obtain for many real-world applications. We observe that in many scenarios, the relationships between different sensor measurements (e.g., location and acceleration) are analytically described by laws of physics (e.g., second-order differential equation). By incorporating such physics constraints, we can guide the denoising process to improve performance even in the absence of ground truth data. In light of this, we design a physics-informed denoising model that leverages the inherent algebraic relationships between different measurements governed by the underlying physics. By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications. We conducted experiments in various domains, including inertial navigation, CO2 monitoring, and HVAC control, and achieved state-of-the-art performance compared with existing denoising methods. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results that closely align with those from high-precision, high-cost alternatives, leading to an efficient, cost-effective approach for more accurate sensor-based systems.

Time-series data contains temporal order information that can guide representation learning for predictive end tasks (e.g., classification, regression). Recently, there are some attempts to leverage such order information to first pre-train time-series models by reconstructing time-series values of randomly masked time segments, followed by an end-task fine-tuning on the same dataset, demonstrating improved end-task performance. However, this learning paradigm decouples data reconstruction from the end task. We argue that the representations learnt in this way are not informed by the end task and may, therefore, be sub-optimal for the end-task performance. In fact, the importance of different timestamps can vary significantly in different end tasks. We believe that representations learnt by reconstructing important timestamps would be a better strategy for improving end-task performance. In this work, we propose TARNet, Task-Aware Reconstruction Network, a new model using Transformers to learn task-aware data reconstruction that augments end-task performance. Specifically, we design a data-driven masking strategy that uses self-attention score distribution from end-task training to sample timestamps deemed important by the end task. Then, we mask out data at those timestamps and reconstruct them, thereby making the reconstruction task-aware. This reconstruction task is trained alternately with the end task at every epoch, sharing parameters in a single model, allowing the representation learnt through reconstruction to improve end-task performance. Extensive experiments on tens of classification and regression datasets show that TARNet significantly outperforms state-of-the-art baseline models across all evaluation metrics.

Scientific discoveries and studies about our physical world have long benefited from large-scale and planetary sensing, from weather forecasting to wildfire monitoring. However, the limited deployment of sensors in the environment due to cost or physical access constraints has lagged behind our ever-growing need for increased data coverage and higher resolution, impeding timely and precise monitoring and understanding of the environment. Therefore, we seek to extend the spatial coverage of analysis based on existing sensory data, that is, to "generate" data for locations where no historical data exists. This problem is fundamentally different and more challenging than the traditional spatio-temporal imputation that assumes data for any particular location are only partially missing across time. Inspired by the success of Generative Adversarial Network (GAN) in imputation, we propose a novel ESC-GAN. We observe that there are local patterns in nearby locations, as well as trends in a global manner (e.g., temperature drops as altitude increases regardless of the location). As local patterns may exhibit at different scales (from meters to kilometers), we employ a multi-branch generator to aggregate information of different granularity. More specifically, each branch in the generator contains 1) randomly masked 3D partial convolutions at different resolutions to capture the local patterns and 2) global attention modules for global similarity. Next, we adversarially train a 3D convolution-based discriminator to distinguish the generator's output from the ground truth. Extensive experiments on three geo-sensor datasets demonstrate that ESC-GAN outperforms state-of-the-art methods on extending spatial coverage and also achieves the best results on a traditional spatio-temporal imputation task.

Discovering patterns in time series data is essential to many key tasks in intelligent sensing systems, such as human activity recognition and event detection. These tasks involve the classification of sensory information from physical measurements such as inertial or temperature change measurements. Due to differences in the underlying physics, existing methods for classification use handcrafted features combined with traditional learning algorithms, or employ distinct deep neural models to directly learn from raw data.

By processing the data in motion, real-time data processing enables us to extract instantaneous results from online input data that ensures timely responsiveness to events as well as a much enhanced capacity to process large data sets. This is especially important when decision loops include querying and processing data on the web where size and latency considerations make it impossible to process raw data in real-time. This makes dimensionality reduction techniques, like principal component analysis (PCA), an important data preprocessing tool to gain insights into data. In this paper, we propose a variant of PCA, that is suited for real-time applications. In the real-time version of the PCA problem, we maintain a window over the most recent data and project every incoming row of data into lower dimensional subspace, which we generate as the output of the model. The goal is to minimize the reconstruction error of the output from the input. We use the reconstruction error as the termination criteria to update the eigenspace as new data arrives. To verify whether our proposed model can capture the essence of the changing distribution of large datasets in real-time, we have implemented the algorithm and evaluated performance against carefully designed simulations that change distributions of data sources over time in a controllable manner. Furthermore, we have demonstrated that our algorithm can capture the changing distributions of real-life datasets by running simulations on datasets from a variety of real-time applications e.g. localization, customer expenditure, etc. We propose algorithmic enhancements that rely upon spectral analysis to improve dimensionality reduction. Results show that our method can successfully capture the changing distribution of data in a real-time scenario, thus enabling real-time PCA.