9 research outputs found
A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets
In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches
cGAN-Based High Dimensional IMU Sensor Data Generation for Therapeutic Activities
Human activity recognition is a core technology for applications such as
rehabilitation, ambient health monitoring, and human-computer interactions.
Wearable devices, particularly IMU sensors, can help us collect rich features
of human movements that can be leveraged in activity recognition. Developing a
robust classifier for activity recognition has always been of interest to
researchers. One major problem is that there is usually a deficit of training
data for some activities, making it difficult and sometimes impossible to
develop a classifier. In this work, a novel GAN network called TheraGAN was
developed to generate realistic IMU signals associated with a particular
activity. The generated signal is of a 6-channel IMU. i.e., angular velocities
and linear accelerations. Also, by introducing simple activities, which are
meaningful subparts of a complex full-length activity, the generation process
was facilitated for any activity with arbitrary length. To evaluate the
generated signals, besides perceptual similarity metrics, they were applied
along with real data to improve the accuracy of classifiers. The results show
that the maximum increase in the f1-score belongs to the LSTM classifier by a
13.27% rise when generated data were added. This shows the validity of the
generated data as well as TheraGAN as a tool to build more robust classifiers
in case of imbalanced data problem
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Generalized Activity Assessment computed fully distributed within a Wireless Body Area Network
Currently available wearables are usually based on a single sensor node with
integrated capabilities for classifying different activities. The next
generation of cooperative wearables could be able to identify not only
activities, but also to evaluate them qualitatively using the data of several
sensor nodes attached to the body, to provide detailed feedback for the
improvement of the execution. Especially within the application domains of
sports and health-care, such immediate feedback to the execution of body
movements is crucial for (re-)learning and improving motor skills. To enable
such systems for a broad range of activities, generalized approaches for human
motion assessment within sensor networks are required. In this paper, we
present a generalized trainable activity assessment chain (AAC) for the online
assessment of periodic human activity within a wireless body area network. AAC
evaluates the execution of separate movements of a prior trained activity on a
fine-grained quality scale. We connect qualitative assessment with human
knowledge by projecting the AAC on the hierarchical decomposition of motion
performed by the human body as well as establishing the assessment on a
kinematic evaluation of biomechanically distinct motion fragments. We evaluate
AAC in a real-world setting and show that AAC successfully delimits the
movements of correctly performed activity from faulty executions and provides
detailed reasons for the activity assessment
Identification of time series components using break for time series components (bftsc) and group for time series components (gftsc) techniques
Commonly in time series modelling, identifying the four time series components which are trend, seasonal, cyclical, and irregular is conducted manually using the time series plot. However, this manual identification approach requires tacit knowledge of the expert forecaster. Thus, an automated identification approach is needed to bridge the gap between expert and end user. Previously, a technique known as Break for Additive Seasonal and Trend (BFAST) was developed to automatically identify only linear trend and seasonal components, and consider the other two (i.e., cyclical and irregular) as random. Therefore, in this study, BFAST was extended to identify all four time series components using two new techniques termed Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC). Both techniques were developed by adding cyclical and irregular components to the previous BFAST technique. The performance of BFTSC and GFTSC were validated through simulation and empirical studies. In the simulation study, monthly and yearly data were replicated 100 times based on three sample sizes (small, medium, and large), and embedding the four time series components as the simulation conditions. Percentages of identifying the correct time series components were calculated in the simulation data. Meanwhile in the empirical study, four data sets were used by comparing the manual identification approach with the BFTSC and GFTSC automatic identification. The simulation findings indicated that BFTSC and GFTSC identified correct time series components 100% when large sample size combined with linear trend and other remaining time series components. The empirical findings also supported BFTSC and GFTSC, which performed as well as a manual identification approach for only two data sets exhibiting linear trend and other components combinations. Both techniques were not performing well in other two data sets displaying curve trend. These findings indicated that BFTSC and GFTSC automatic identification techniques are suitable for data with linear trend and require future extensions for other trends. The proposed techniques help end user in reducing time to automatically identify the time series component
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A survey on wearable sensor modality centred human activity recognition in health care
Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR
Physical Activity Recognition and Identification System
Background: It is well-established that physical activity is beneficial to health. It is less known how the characteristics of physical activity impact health independently of total amount. This is due to the inability to measure these characteristics in an objective way that can be applied to large population groups. Accelerometry allows for objective monitoring of physical activity but is currently unable to identify type of physical activity accurately. Methods: This thesis details the creation of an activity classifier that can identify type from accelerometer data. The current research in activity classification was reviewed and methodological challenges were identified. The main challenge was the inability of classifiers to generalize to unseen data. Creating methods to mitigate this lack of generalisation represents the bulk of this thesis. Using the review, a classification pipeline was synthesised, representing the sequence of steps that all activity classifiers use. 1. Determination of device location and setting (Chapter 4) 2. Pre-processing (Chapter 5) 3. Segmenting into windows (Chapters 6) 4. Extracting features (Chapters 7,8) 5. Creating the classifier (Chapter 9) 6. Post-processing (Chapter 5) For each of these steps, methods were created and tested that allowed for a high level of generalisability without sacrificing overall performance. Results: The work in this thesis results in an activity classifier that had a good ability to generalize to unseen data. The classifier achieved an F1-score of 0.916 and 0.826 on data similar to its training data, which is statistically equivalent to the performance of current state of the art models (0.898, 0.765). On data dissimilar to its training data, the classifier achieved a significantly higher performance than current state of the art methods (0.759, 0.897 versus 0.352, 0.415). This shows that the classifier created in this work has a significantly greater ability to generalise to unseen data than current methods. Conclusion: This thesis details the creation of an activity classifier that allows for an improved ability to generalize to unseen data, thus allowing for identification of type from acceleration data. This should allow for more detailed investigation into the specific health effects of type in large population studies utilising accelerometers