We envision that wearables of the future must be computationally autonomous in the sense that their underlying computational algorithms reconfigure automatically without need for collecting any new labeled training data. In this project, we investigate development of multi-view learning algorithms that enable the vision of computationally autonomous wearables and result in highly sustainable and scalable wearables of the future. The algorithms and tools are validated through both in-lab experiments and using data collected in uncontrolled environments.
Related Research Papers:
LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition
Activity recognition is central to many motion analysis applications ranging from health assessment to gaming. However, the need for obtaining sufficiently large amounts of labeled data has limited the development of personalized activity recognition models. Semi-supervised learning has traditionally been a promising approach in many application domains to alleviate reliance on large amounts of labeled data by learning the label information from a small set of seed labels. Nonetheless, existing approaches perform poorly in highly dynamic settings, such as wearable systems, because algorithms rely on predefined hyper-parameters or distribution models that need to be tuned for each user or context. To address these challenges, we introduce LabelForest, a novel non-parametric semi-supervised learning framework for activity recognition. LabelForest has two algorithms at its core: (1) a spanning forest algorithm for sample selection and label inference; and (2) a silhouette-based filtering method to finalize label augmentation for machine learning model training.
Personalization without User Interruption: Boosting Accuracy in New Subjects
Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short-term and/or long-term behavioral patterns for the purpose of improving the health and well-being of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms a user-independent model, collecting labels from every single user is burdensome and in some cases impractical. In this research, we propose an uninformed cross-subject transfer learning algorithm that leverages the cross-user similarities by constructing a network-based feature-level representation of the data in source user (i.e., the one who has participated in labeling the physical activities) and target users (i.e., the future users who will not provide any ground-truth labels but wish to utilize the system at the same activity recognition accuracy of source user model). To this end, we perform a best-effort community detection to extract the core observations in target data. Our algorithm uses a heuristic classifier-based mapping to assign activity labels to the core observations. Finally, the output of labeling is conditionally fused with the prediction of the source-model to develop a boosted and personalized activity recognition algorithm. Our analysis on real-world data demonstrates the superiority of our approach. While the proposed system has been designed and verified for activity recognition task, it can be easily generalized to various machine learning based applications of pervasive computing and internet-of-things.
Plug-n-Learn: Autonomous Training of New Sensors
In this research, we introduce the concept of plug-n-learn for human-centered IoT applications where machine learning algorithms are reconfigured automatically, in real-time, and without need for any new training data. Our pilot application in this research is activity recognition where the goal is to detect physical movements of the user based on wearable sensor measurements. We address this problem by proposing a novel method to transfer activity recognition capabilities of one sensor to another where the collective network of the two sensors achieves much higher accuracy performance. Our approach allows to transfer machine learning knowledge from an existing sensor, called source view, to a new sensor, called target view, and these capabilities are augmented through the sensing observations made by the target view. We call this approach a Multi-view Learing and formulate this problem using Linear Programming, introduce a graph modeling of the problem, and propose a greedy heuristic to solve the problem. We evaluate the performance of the proposed automatic learning approach using real data collected with wearable motion sensors.
Autonomous Learning of Sensor Location and Context
In this research, we take first steps in developing automatic and real-time training of sensor-context detection without labeled training data. Specifically, we focus on cases where multiple context-specific algorithms (i.e., ‘expert models’) are shared for use in a dynamic view where the sensor is worn/used on various body locations each representing one sensor-context. We propose an approach for learning a gating function for choosing the most accurate expert model based on the observed sensor data. Our approach, called Synchronous Sensor-Context Learning (SSCL), first generates and automatically labels a training datasets by examining observations of the dynamic sensor and associating those observations with synchronously sampled observations of a static sensor node. This training dataset is then used to learn the gating function for expert model activation. Our multi-view learning approach presented in this research is a novel method for sharing activity recognition capabilities of several sensors, with already training activity recognition classifiers, for use by a dynamic sensor, which does not have any previously trained activity recognition model. Our approach allows to transfer machine learning knowledge from an existing sensor, called static view, to a new sensor, called dynamic view, and combine the knowledge with already shared capabilities and develop an extensive model for the dynamic view.