85 research outputs found
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
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
DeepEar: Robust smartphone audio sensing in unconstrained acoustic environments using deep learning
Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar â the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6% of the smartphoneâs battery dailyThis is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2750858.280426
From smart to deep: Robust activity recognition on smartwatches using deep learning
The use of deep learning for the activity recognition performed by wearables, such as smartwatches, is an understudied problem. To advance current understanding in this area, we perform a smartwatch-centric investigation of activity recognition under one of the most popular deep learning methods - Restricted Boltzmann Machines (RBM). This study includes a variety of typical behavior and context recognition tasks related to smartwatches (such as transportation mode, physical activities and indoor/outdoor detection) to which RBMs have previously never been applied. Our findings indicate that even a relatively simple RBM-based activity recognition pipeline is able to outperform a wide-range of common modeling alternatives for all tested activity classes. However, usage of deep models is also often accompanied by resource consumption that is unacceptably high for constrained devices like watches. Therefore, we complement this result with a study of the overhead of specifically RBM-based activity models on representative smartwatch hardware (the Snapdragon 400 SoC, present in many commercial smartwatches). These results show, contrary to expectation, RBM models for activity recognition have acceptable levels of resource use for smartwatch-class hardware already on the market. Collectively, these two experimental results make a strong case for more widespread adoption of deep learning techniques within smartwatch designs moving forward
User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation
Kan bare brukes i forskningssammenheng, ikke kommersielt. Les mer her: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsBuilding predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic humanâmachine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each usersâ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the modelsâ performance.acceptedVersio
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey
Machine learning-based wearable human activity recognition (WHAR) models
enable the development of various smart and connected community applications
such as sleep pattern monitoring, medication reminders, cognitive health
assessment, sports analytics, etc. However, the widespread adoption of these
WHAR models is impeded by their degraded performance in the presence of data
distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and
environmental diversities. Various traditional machine learning algorithms and
transfer learning techniques have been proposed in the literature to address
the underpinning challenges of handling such data heterogeneities. Domain
adaptation is one such transfer learning techniques that has gained significant
popularity in recent literature. In this paper, we survey the recent progress
of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based
human activity recognition area, discuss potential future directions
Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor
Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.publishedVersio
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