8,548 research outputs found
Activity recognition in mental health monitoring using multi-channel data collection and neural network
Treball de Final de MĂ ster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadĂšmic 2020-2021Ecological momentary assessment (EMA) methods can be used to extract context related information by studying a subjectâs behaviour in an environment in real-time. In mental health EMA can be used to assess patients with mental disorders by deriving contextual information from data and provide psychological interventions based on the behaviour of the person. With the advancements in technology smart devices such as mobile phone and smartwatch can be used to collect EMA data. Such a contextual information system is used in SyMptOMS, which uses accelerometer data from smartphone for activity recognition of the patient. Monitoring patients with mental disorders can be useful and psychological interventions can be provided in real time to control their behavior. In this research study, we aim to investigate the effect of multi-channel data on the accuracy of human activity recognition using neural network model by predicting activities based on data from smartphone and smartwatch accelerometer sensors. In addition to this the study investigates model performance for similar activities such as SITTING and LYING DOWN. Tri-axial accelerometer data is collected at the same time from smartphone and smartwatch using a data collection application. Features are extracted from the raw data and then used as input to a neural network. The model is trained for single data input from smartphone and smartwatch as well the data from sensor fusion. The performance of the model is evaluated by using test samples from collected data. Results show that model with multi-channel data achieves a higher accuracy of activity recognition than the model with only single-channel data source
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
Mining user activity as a context source for search and retrieval
Nowadays in information retrieval it is generally accepted that if we can better
understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
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