Deep learning for sensor-based activity recognition: A survey

Human activity recognition (HAR) is currently recognized as a key element of a more general framework designed to perform continuous monitoring of human behaviors in the area of ambient assisted living (AAL), well-being management, medical diagnosis, elderly care, rehabilitation, entertainment, and surveillance in smart home environments. In this paper, an innovative HAR system, exploiting the potential of wearable devices integrated with the skills of deep learning techniques, is presented with the aim of recognizing the most common daily activities of a person at home. The designed wearable sensor embeds an inertial measurement unit (IMU) and a Wi-Fi section to send data on a cloud service and to allow direct connection to the Internet through a common home router so that the user themselves could manage the installation procedure. The sensor is coupled to a convolutional neural network (CNN) network designed to make inferences with the minimum possible resources to keep open the way of its implementation on low-cost or embedded devices. The system is conceived for daily activity monitor and nine different activities can be highlighted with an accuracy of 97%.

IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly’s daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Deep Recurrent Neural Networks for Human Activity Recognition.

Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spaces, as well as improved function approximation capability, the new approaches are able to solve harder problems, permitting reward functions that are better aligned with the actual application goals. We show such a reward function and use policy-gradient approaches to learn capable policies, leading to behavior more appropriate for IoT nodes with less manual design effort, increasing the level of autonomy in IoT.

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Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measurements would provide little new information. This is a highly general approach, which allows for a wide range of use cases without significant human design effort or hyperparameter tuning. We illustrate the approach in a scenario of workplace noise monitoring, where results show that the learned behavior outperforms a uniform sampling strategy and comes close to a near-optimal oracle solution.

Information-driven adaptive sensing based on deep reinforcement learning

We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge reinforcement learning algorithms and exemplify our results with the autonomous control of a solar-powered IoT device.

IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning

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Abdulmajid Murad

Papers

Deep Recurrent Neural Networks for Human Activity Recognition.

Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

Information-driven adaptive sensing based on deep reinforcement learning

IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning

Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning