95,554 research outputs found
RH-HAR-SK: A Multi-view Dataset with Skeleton Data for Ambient Assisted Living Research
© 2023 IARIA.Human and activity detection has always been a vital task in Human-Robot Interaction (HRI) scenarios, such as those involving assistive robots. In particular, skeleton-based Human Activity Recognition (HAR) offers a robust and effective detection method based on human biomechanics. Recent advancements in human pose estimation have made it possible to extract skeleton positioning data accurately and quickly using affordable cameras. In interaction with a human, robots can therefore capture detailed information from a close distance and flexible perspective. However, recognition accuracy is susceptible to robot movements, where the robot often fails to capture the entire scene. To address this we propose the adoption of external cameras to improve the accuracy of activity recognition on a mobile robot. In support of this proposal, we present the dataset RH-HAR-SK that combines multiple camera perspectives augmented with human skeleton extraction obtained by the HRNet pose estimation. We apply qualitative and quantitative analysis techniques to the extracted skeleton and its joints to demonstrate the additional value of external cameras to the robot's recognition pipeline. Results show that while the robot's camera can provide optimal recognition accuracy in some specific scenarios, an external camera increases overall performance
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data
The current gold standard for human activity recognition (HAR) is based on
the use of cameras. However, the poor scalability of camera systems renders
them impractical in pursuit of the goal of wider adoption of HAR in mobile
computing contexts. Consequently, researchers instead rely on wearable sensors
and in particular inertial sensors. A particularly prevalent wearable is the
smart watch which due to its integrated inertial and optical sensing
capabilities holds great potential for realising better HAR in a non-obtrusive
way. This paper seeks to simplify the wearable approach to HAR through
determining if the wrist-mounted optical sensor alone typically found in a
smartwatch or similar device can be used as a useful source of data for
activity recognition. The approach has the potential to eliminate the need for
the inertial sensing element which would in turn reduce the cost of and
complexity of smartwatches and fitness trackers. This could potentially
commoditise the hardware requirements for HAR while retaining the functionality
of both heart rate monitoring and activity capture all from a single optical
sensor. Our approach relies on the adoption of machine vision for activity
recognition based on suitably scaled plots of the optical signals. We take this
approach so as to produce classifications that are easily explainable and
interpretable by non-technical users. More specifically, images of
photoplethysmography signal time series are used to retrain the penultimate
layer of a convolutional neural network which has initially been trained on the
ImageNet database. We then use the 2048 dimensional features from the
penultimate layer as input to a support vector machine. Results from the
experiment yielded an average classification accuracy of 92.3%. This result
outperforms that of an optical and inertial sensor combined (78%) and
illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive
Scienc
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
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Understanding face and eye visibility in front-facing cameras of smartphones used in the wild
Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations
- …