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  1. Missing data is a very common challenge in health monitoring systems and one reason for that is that they are largely dependent on different types of sensors. A critical characteristic of the sensor-based prediction systems is their dependency on hardware, which is prone to physical limitations that add another layer of complexity to the algorithmic component of the system. For instance, it might not be realistic to assume that the prediction model has access to all sensors at all times. This can happen in the real-world setup if one or more sensors on a device malfunction or temporarily have to be disabled due to power limitations. The consequence of such a scenario is that the model faces ``missing input data'' from those unavailable sensors at the deployment time, and as a result, the quality of prediction can degrade significantly. While the missing input data is a very well-known problem, to the best of our knowledge, no study has been done to efficiently minimize the performance drop when one or more sensors may be unavailable for a significant amount of time. The sensor failure problem investigated in this paper can be viewed as a spatial missing data problem, which has not been explored to date. In this work, we show that the naive known methods of dealing with missing input data such as zero-filling or mean-filling are not suitable for sensor-based prediction and we propose an algorithm that can reconstruct the missing input data for unavailable sensors. Moreover, we show that on the MobiAct, MotionSense, and MHEALTH activity classification benchmarks, our proposed method can outperform the baselines by large accuracy margins of 8.2%, 15.1%, and 11.6%, respectively. 
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  2. The recent success of deep neural networks in prediction tasks on wearable sensor data is evident. However, in more practical online learning scenarios, where new data arrive sequentially, neural networks suffer severely from the ``catastrophic forgetting`` problem. In real-world settings, given a pre-trained model on the old data, when we collect new data, it is practically infeasible to re-train the model on both old and new data because the computational costs will increase dramatically as more and more data arrive in time. However, if we fine-tune the model only with the new data because the new data might be different from the old data, the neural network parameters will change to fit the new data. As a result, the new parameters are no longer suitable for the old data. This phenomenon is known as catastrophic forgetting, and continual learning research aims to overcome this problem with minimal computational costs. While most of the continual learning research focuses on computer vision tasks, implications of catastrophic forgetting in wearable computing research and potential avenues to address this problem have remained unexplored. To address this knowledge gap, we study continual learning for activity recognition using wearable sensor data. We show that the catastrophic forgetting problem is a critical challenge for real-world deployment of machine learning models for wearables. Moreover, we show that the catastrophic forgetting problem can be alleviated by employing various training techniques. 
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  3. Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and sleep quality assessment. However, there is limited research on development of accurate and efficient lying posture tracking models using wrist-based sensor. Our experiments demonstrate a major drop in the accuracy of the lying posture tracking using wrist-based accelerometer sensor due to the unpredictable noise from arbitrary wrist movements and rotations while sleeping. In this paper, we develop a deep transfer learning method that improves performance of lying posture tracking using noisy data from wrist sensor by transferring the knowledge from an initial setting which contains both clean and noisy data. The proposed solution develops an optimal mapping model from the noisy data to the clean data in the initial setting using LSTM sequence regression, and reconstruct clean synthesized data in another setting where no noisy sensor data is available. This increases the lying posture tracking F1-Score by 24.9% for left-wrist and by 18.1% for right-wrist sensors comparing to the case without mapping. 
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  4. Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for neural networks typically leads to catastrophic forgetting. That is, the solution found for a subsequent task does not perform well on the previous ones anymore. However, the relationship between the different minima that the two training regimes arrive at is not well understood. What sets them apart? Is there a local structure that could explain the difference in performance achieved by the two different schemes? Motivated by recent work showing that different minima of the same task are typically connected by very simple curves of low error, we investigate whether multitask and continual solutions are similarly connected. We empirically find that indeed such connectivity can be reliably achieved and, more interestingly, it can be done by a linear path, conditioned on having the same initialization for both. We thoroughly analyze this observation and discuss its significance for the continual learning process. Furthermore, we exploit this finding to propose an effective algorithm that constrains the sequentially learned minima to behave as the multitask solution. We show that our method outperforms several state of the art continual learning algorithms on various vision benchmarks. 
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  5. null (Ed.)
    With the recent advances in both machine learning and embedded systems research, the demand to deploy computational models for real-time execution on edge devices has increased substantially. Without deploying computational models on edge devices, the frequent transmission of sensor data to the cloud results in rapid battery draining due to the energy consumption of wireless data transmission. This rapid power dissipation leads to a considerable reduction in the battery lifetime of the system, therefore jeopardizing the real-world utility of smart devices. It is well-established that for difficult machine learning tasks, models with higher performance often require more computation power and thus are not power-efficient choices for deployment on edge devices. However, the trade-offs between performance and power consumption are not well studied. While numerous methods (e.g., model compression) have been developed to obtain an optimal model, these methods focus on improving the efficiency of a single model. In an entirely new direction, we introduce an effective method to find a combination of multiple models that are optimal in terms of power-efficiency and performance by solving an optimization problem in which both performance and power consumption are taken into account. Experimental results demonstrate that on the ImageNet dataset, we can achieve a 20% energy reduction with only 0.3% accuracy drop compared to Squeeze-and-Excitation Networks. Compared to a pruned convolutional neural network for human activity recognition, while consuming 1.7% less energy, our proposed policy achieves 1.3% higher accuracy. 
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  6. In recent years, neural networks have demonstrated an outstanding ability to achieve complex learning tasks across various domains. However, they suffer from the "catastrophic forgetting" problem when they face a sequence of learning tasks, where they forget the old ones as they learn new tasks. This problem is also highly related to the "stability-plasticity dilemma". The more plastic the network, the easier it can learn new tasks, but the faster it also forgets previous ones. Conversely, a stable network cannot learn new tasks as fast as a very plastic network. However, it is more reliable to preserve the knowledge it has learned from the previous tasks. Several solutions have been proposed to overcome the forgetting problem by making the neural network parameters more stable, and some of them have mentioned the significance of dropout in continual learning. However, their relationship has not been sufficiently studied yet. In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active. Our experiments show that the stability achieved by this implicit gating plays a very critical role in leading to performance comparable to or better than other involved continual learning algorithms to overcome catastrophic forgetting. 
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  7. null (Ed.)
    Catastrophic forgetting affects the training of neural networks, limiting their ability to learn multiple tasks sequentially. From the perspective of the well established plasticity-stability dilemma, neural networks tend to be overly plastic, lacking the stability necessary to prevent the forgetting of previous knowledge, which means that as learning progresses, networks tend to forget previously seen tasks. This phenomenon coined in the continual learning literature, has attracted much attention lately, and several families of approaches have been proposed with different degrees of success. However, there has been limited prior work extensively analyzing the impact that different training regimes -- learning rate, batch size, regularization method-- can have on forgetting. In this work, we depart from the typical approach of altering the learning algorithm to improve stability. Instead, we hypothesize that the geometrical properties of the local minima found for each task play an important role in the overall degree of forgetting. In particular, we study the effect of dropout, learning rate decay, and batch size on forming training regimes that widen the tasks' local minima and consequently, on helping it not to forget catastrophically. Our study provides practical insights to improve stability via simple yet effective techniques that outperform alternative baselines. 
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  8. While inferring human activities from sensors embedded in mobile devices using machine learning algorithms has been studied, current research relies primarily on sensor data that are collected in controlled settings often with healthy individuals. Currently, there exists a gap in research about how to design activity recognition models based on sensor data collected with chronically-ill individuals and in free-living environments. In this paper, we focus on a situation where free-living activity data are collected continuously, activity vocabulary (i.e., class labels) are not known as a priori, and sensor data are annotated by end-users through an active learning process. By analyzing sensor data collected in a clinical study involving patients with cardiovascular disease, we demonstrate significant challenges that arise while inferring physical activities in uncontrolled environments. In particular, we observe that activity labels that are distinct in syntax can refer to semantically-identical behaviors, resulting in a sparse label space. To construct a meaningful label space, we propose LabelMerger, a framework for restructuring the label space created through active learning in uncontrolled environments in preparation for training activity recognition models. LabelMerger combines the semantic meaning of activity labels with physical attributes of the activities (i.e., domain knowledge) to generate a flexible and meaningful representation of the labels. Specifically, our approach merges labels using both word embedding techniques from the natural language processing domain and activity intensity from the physical activity research. We show that the new representation of the sensor data obtained by LabelMerger results in more accurate activity recognition models compared to the case where original label space is used to learn recognition models. 
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