1,992 research outputs found
A framework for learning analytics using commodity wearable devices
We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55)
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
Nowadays, video cameras are deployed in large scale for spatial monitoring of
physical places (e.g., surveillance systems in the context of smart cities).
The massive camera deployment, however, presents new challenges for analyzing
the enormous data, as the cost of high computational overhead of sophisticated
deep learning techniques imposes a prohibitive overhead, in terms of energy
consumption and processing throughput, on such resource-constrained edge
devices. To address these limitations, this paper envisions a collaborative
intelligent cross-camera video analytics paradigm at the network edge in which
camera nodes adjust their pipelines (e.g., inference) to incorporate correlated
observations and shared knowledge from other nodes' contents. By harassing
redundant spatio-temporal to reduce the size of the inference search space in
one hand, and intelligent collaboration between video nodes on the other, we
discuss how such collaborative paradigm can considerably improve accuracy,
reduce latency and decrease communication bandwidth compared to
non-collaborative baselines. This paper also describes major opportunities and
challenges in realizing such a paradigm.Comment: First International Workshop on Challenges in Artificial Intelligence
and Machine Learnin
IoT Platform for COVID-19 Prevention and Control: A Survey
As a result of the worldwide transmission of severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has
evolved into an unprecedented pandemic. Currently, with unavailable
pharmaceutical treatments and vaccines, this novel coronavirus results in a
great impact on public health, human society, and global economy, which is
likely to last for many years. One of the lessons learned from the COVID-19
pandemic is that a long-term system with non-pharmaceutical interventions for
preventing and controlling new infectious diseases is desirable to be
implemented. Internet of things (IoT) platform is preferred to be utilized to
achieve this goal, due to its ubiquitous sensing ability and seamless
connectivity. IoT technology is changing our lives through smart healthcare,
smart home, and smart city, which aims to build a more convenient and
intelligent community. This paper presents how the IoT could be incorporated
into the epidemic prevention and control system. Specifically, we demonstrate a
potential fog-cloud combined IoT platform that can be used in the systematic
and intelligent COVID-19 prevention and control, which involves five
interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring,
Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and
SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art
literatures of these five interventions to present the capabilities of IoT in
countering against the current COVID-19 pandemic or future infectious disease
epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa
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