CAVIAR: Context-driven Active and Incremental Activity Recognition

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process

Radar Based Activity Recognition using CNN-LSTM Network Architecture

Human Activity Recognition based research has got intensified based on the evolving demand of smart systems. There has been already a lot of wearables, digital smart sensors deployed to classify various activities. Radar sensor-based Activity recognition has been an active research area during recent times. In order to classify the radar micro doppler signature images we have proposed a approach using Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Convolutional Layer is used to update the filter values to learn the features of the radar images. LSTM Layer enhances the temporal information besides the features obtained through Convolutional Neural Network. We have used a dataset published by University of Glasgow that captures six activities for 56 subjects under different ages, which is a first of its kind dataset unlike the signals captured under controlled lab environment. Our Model has achieved 96.8% for the training data and 93.5% for the testing data. The proposed work has outperformed the existing traditional deep learning Architectures

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works

Synthetic Sensor Data for Human Activity Recognition

Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance. In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier

Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition

Human activity recognition (HAR) is an active field of research for the classification of human movements and applications in a wide variety of areas such as medical diagnosis, health care systems, elderly care, rehabilitation, surveillance in a smart home, and so on. HAR data are collected from wearable devices which include different types of sensors and/or with the smartphone sensor's aid. In recent years, deep learning algorithms have been showed a significant robustness for classifying human activities on HAR data. In the architecture of such deep learning networks, there are several hyperparameters to control the model efficiency which are mainly set by experiment. In this paper, firstly, we introduced one dimensional Convolutional neural network (CNN) as a model among supervised deep learning for an online HAR data classification. In order to automatically choose the optimum hyperparameters of the CNN model, seven approaches based on metaheuristic algorithms were investigated. The optimization algorithms were evaluated on the HAR dataset from the UCI Machine Learning repository. Furthermore, the performance of the proposed method was compared with several state-of-the-art evolutionary algorithms and other deep learning models. The experimental results showed the robustness of using metaheuristic algorithms to optimize the hyperparameters in CNN

A machine vision approach to human activity recognition using photoplethysmograph sensor data

Human activity recognition (HAR) is an active area of research concerned with the classification of human motion. Cameras are the gold standard used in this area, but they are proven to have scalability and privacy issues. HAR studies have also been conducted with wearable devices consisting of inertial sensors. Perhaps the most common wearable, smart watches, comprising of inertial and optical sensors, allow for scalable, non-obtrusive studies. We are seeking to simplify this wearable approach further by determining if wrist-mounted optical sensing, usually used for heart rate determination, can also provide useful data for relevant activity recognition. If successful, this could eliminate the need for the inertial sensor, and so simplify the technological requirements in wearable HAR. We adopt a machine vision approach for activity recognition based on plots of the optical signals so as to produce classifications that are easily explainable and interpretable by non-technical users. Specifically, time-series images of photoplethysmography signals are used to retrain the penultimate layer of a pretrained convolutional neural network leveraging the concept of transfer learning. Our results demonstrate an average accuracy of 75.8%. This illustrates the feasibility of implementing an optical sensor-only solution for a coarse activity and heart rate monitoring system. Implementing an optical sensor only in the design of these wearables leads to a trade off in classification performance, but in turn, grants the potential to simplify the overall design of activity monitoring and classification systems in the future

Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user's contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals' perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users' Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

SYSTEM-ON-A-CHIP (SOC)-BASED HARDWARE ACCELERATION FOR HUMAN ACTION RECOGNITION WITH CORE COMPONENTS

Today, the implementation of machine vision algorithms on embedded platforms or in portable systems is growing rapidly due to the demand for machine vision in daily human life. Among the applications of machine vision, human action and activity recognition has become an active research area, and market demand for providing integrated smart security systems is growing rapidly. Among the available approaches, embedded vision is in the top tier; however, current embedded platforms may not be able to fully exploit the potential performance of machine vision algorithms, especially in terms of low power consumption. Complex algorithms can impose immense computation and communication demands, especially action recognition algorithms, which require various stages of preprocessing, processing and machine learning blocks that need to operate concurrently. The market demands embedded platforms that operate with a power consumption of only a few watts. Attempts have been mad to improve the performance of traditional embedded approaches by adding more powerful processors; this solution may solve the computation problem but increases the power consumption. System-on-a-chip eld-programmable gate arrays (SoC-FPGAs) have emerged as a major architecture approach for improving power eciency while increasing computational performance. In a SoC-FPGA, an embedded processor and an FPGA serving as an accelerator are fabricated in the same die to simultaneously improve power consumption and performance. Still, current SoC-FPGA-based vision implementations either shy away from supporting complex and adaptive vision algorithms or operate at very limited resolutions due to the immense communication and computation demands. The aim of this research is to develop a SoC-based hardware acceleration workflow for the realization of advanced vision algorithms. Hardware acceleration can improve performance for highly complex mathematical calculations or repeated functions. The performance of a SoC system can thus be improved by using hardware acceleration method to accelerate the element that incurs the highest performance overhead. The outcome of this research could be used for the implementation of various vision algorithms, such as face recognition, object detection or object tracking, on embedded platforms. The contributions of SoC-based hardware acceleration for hardware-software codesign platforms include the following: (1) development of frameworks for complex human action recognition in both 2D and 3D; (2) realization of a framework with four main implemented IPs, namely, foreground and background subtraction (foreground probability), human detection, 2D/3D point-of-interest detection and feature extraction, and OS-ELM as a machine learning algorithm for action identication; (3) use of an FPGA-based hardware acceleration method to resolve system bottlenecks and improve system performance; and (4) measurement and analysis of system specications, such as the acceleration factor, power consumption, and resource utilization. Experimental results show that the proposed SoC-based hardware acceleration approach provides better performance in terms of the acceleration factor, resource utilization and power consumption among all recent works. In addition, a comparison of the accuracy of the framework that runs on the proposed embedded platform (SoCFPGA) with the accuracy of other PC-based frameworks shows that the proposed approach outperforms most other approaches