1,306 research outputs found
Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization
The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de EconomÃa y CompetitividadXunta de Galici
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
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Intuitive Human-Machine Interfaces for Non-Anthropomorphic Robotic Hands
As robots become more prevalent in our everyday lives, both in our workplaces and in our homes, it becomes increasingly likely that people who are not experts in robotics will be asked to interface with robotic devices. It is therefore important to develop robotic controls that are intuitive and easy for novices to use. Robotic hands, in particular, are very useful, but their high dimensionality makes creating intuitive human-machine interfaces for them complex. In this dissertation, we study the control of non-anthropomorphic robotic hands by non-roboticists in two contexts: collaborative manipulation and assistive robotics.
In the field of collaborative manipulation, the human and the robot work side by side as independent agents. Teleoperation allows the human to assist the robot when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator’s hand as an input device can provide an intuitive control method, but finding a mapping between a human hand and a non-anthropomorphic robot hand can be difficult, due to the hands’ dissimilar kinematics. In this dissertation, we seek to create a mapping between the human hand and a fully actuated, non-anthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users.
We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We first propose the general concept of the subspace, its properties and the variables needed to map from the human hand to a robot hand. We then propose three ways to populate the teleoperation subspace mapping. Two of our mappings use a dataglove to harvest information about the user's hand. We define the mapping between joint space and teleoperation subspace with an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and with an algorithmic definition, which is kinematically independent, and uses objects to define the subspace. Our third mapping for the teleoperation subspace uses forearm electromyography (EMG) as a control input.
Assistive orthotics is another area of robotics where human-machine interfaces are critical, since, in this field, the robot is attached to the hand of the human user. In this case, the goal is for the robot to assist the human with movements they would not otherwise be able to achieve. Orthotics can improve the quality of life of people who do not have full use of their hands. Human-machine interfaces for assistive hand orthotics that use EMG signals from the affected forearm as input are intuitive and repeated use can strengthen the muscles of the user's affected arm. In this dissertation, we seek to create an EMG based control for an orthotic device used by people who have had a stroke. We would like our control to enable functional motions when used in conjunction with a orthosis and to be robust to changes in the input signal.
We propose a control for a wearable hand orthosis which uses an easy to don, commodity forearm EMG band. We develop an supervised algorithm to detect a user’s intent to open and close their hand, and pair this algorithm with a training protocol which makes our intent detection robust to changes in the input signal. We show that this algorithm, when used in conjunction with an orthosis over several weeks, can improve distal function in users. Additionally, we propose two semi-supervised intent detection algorithms designed to keep our control robust to changes in the input data while reducing the length and frequency of our training protocol
Multi-sensor data fusion and modelling in mobile devices for enhanced user experience
Multi-sensor data fusion and modelling in mobile devices for enhanced user experienc
Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens
Current guidelines from the World Health Organization indicate that the
SARS-CoV-2 coronavirus, which results in the novel coronavirus disease
(COVID-19), is transmitted through respiratory droplets or by contact. Contact
transmission occurs when contaminated hands touch the mucous membrane of the
mouth, nose, or eyes so hands hygiene is extremely important to prevent the
spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation
of wearable devices, such as smartwatches, containing acceleration, rotation,
magnetic field sensors, etc., together with the modern technologies of
artificial intelligence, such as machine learning and more recently
deep-learning, allow the development of accurate applications for recognition
and classification of human activities such as: walking, climbing stairs,
running, clapping, sitting, sleeping, etc. In this work, we evaluate the
feasibility of a machine learning based system which, starting from inertial
signals collected from wearable devices such as current smartwatches,
recognizes when a subject is washing or rubbing its hands. Preliminary results,
obtained over two different datasets, show a classification accuracy of about
95% and of about 94% for respectively deep and standard learning techniques
Posture Recognition Using the Interdistances Between Wearable Devices
Recognition of user's postures and activities is particularly important, as it allows applications to customize their operations according to the current situation. The vast majority of available solutions are based on wearable devices equipped with accelerometers and gyroscopes. In this article, a different approach is explored: The posture of the user is inferred from the interdistances between the set of devices worn by the user. Interdistances are first measured by using ultra-wideband transceivers operating in two-way ranging mode and then provided as input to a classifier that estimates current posture. An experimental evaluation shows that the proposed method is effective (up to ∼98.2% accuracy), especially when using a personalized model. The method could be used to enhance the accuracy of activity recognition systems based on inertial sensors
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
We investigate whether a classifier can continuously authenticate users based
on the way they interact with the touchscreen of a smart phone. We propose a
set of 30 behavioral touch features that can be extracted from raw touchscreen
logs and demonstrate that different users populate distinct subspaces of this
feature space. In a systematic experiment designed to test how this behavioral
pattern exhibits consistency over time, we collected touch data from users
interacting with a smart phone using basic navigation maneuvers, i.e., up-down
and left-right scrolling. We propose a classification framework that learns the
touch behavior of a user during an enrollment phase and is able to accept or
reject the current user by monitoring interaction with the touch screen. The
classifier achieves a median equal error rate of 0% for intra-session
authentication, 2%-3% for inter-session authentication and below 4% when the
authentication test was carried out one week after the enrollment phase. While
our experimental findings disqualify this method as a standalone authentication
mechanism for long-term authentication, it could be implemented as a means to
extend screen-lock time or as a part of a multi-modal biometric authentication
system.Comment: to appear at IEEE Transactions on Information Forensics & Security;
Download data from http://www.mariofrank.net/touchalytics
Identification of Persons and Several Demographic Features based on Motion Analysis of Various Daily Activities using Wearable Sensors
In recent years, there has been an increasing interest in using the capabilities of wearable sensors, including
accelerometers, gyroscopes and magnetometers, to recognize individuals while undertaking a set of normal daily
activities. The past few years have seen considerable research exploring person recognition using wearable sensing
devices due to its significance in different applications, including security and human-computer interaction
applications.
This thesis explores the identification of subjects and related multiple biometric demographic attributes based on the
motion data of normal daily activities gathered using wearable sensor devices. First, it studies the recognition of 18
subjects based on motion data of 20 daily living activities using six wearable sensors affixed to different body
locations. Next, it investigates the task of classifying various biometric demographic features: age, gender, height,
and weight based on motion data of various activities gathered using two types of accelerometers and one gyroscope
wearable sensors. Initially, different significant parameters that impact the subjects' recognition success rates are
investigated. These include studying the performance of the three sensor sources: accelerometer, gyroscope, and
magnetometer, and the impact of their combinations. Furthermore, the impact of the number of different sensors
mounted at different body positions and the best body position to mount sensors are also studied. Next, the analysis
also explored which activities are more suitable for subject recognition, and lastly, the recognition success rates and
mutual confusion among individuals. In addition, the impact of several fundamental factors on the classification
performance of different demographic features using motion data collected from three sensors is studied. Those
factors include the performance evaluation of feature-set extracted from both time and frequency domains, feature
selection, individual sensor sources and multiple sources.
The key findings are: (I) Features extracted from all three sensor sources provide the highest accuracy of subjects
recognition. (2) The recognition accuracy is affected by the body position and the number of sensors. Ankle, chest,
and thigh positions outperform other positions in terms of the recognition accuracy of subjects. There is a
depreciating association between the subject classification accuracy and the number of sensors used. (3) Sedentary
activities such as watching tv, texting on the phone, writing with a pen, and using pc produce higher classification
results and distinguish persons efficiently due to the absence of motion noise in the signal. (4) Identifiability is not
uniformly distributed across subjects. (5) According to the classification results of considered biometric features,
both full and selected features-set derived from all three sources of two accelerometers and a gyroscope sensor
provide the highest classification accuracy of all biometric features compared to features derived from individual
sensors sources or pairs of sensors together. (6) Under all configurations and for all biometric features classified; the
time-domain features examined always outperformed the frequency domain features. Combining the two sets led to
no increase in classification accuracy over time-domain alone
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