Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living

Weiss, Gary M.; Yoneda, Kenichi; Hayajneh, Thaier

doi:10.1109/access.2019.2940729

Cited by 284 publications

(181 citation statements)

References 32 publications

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“…The WISDM dataset [11], [12] was released late 2019 by the Fordham University. It was collected using accelerometer and gyroscope sensors of a smartphone (Galaxy Nexus or Samsung Galaxy S5) and smartwatch as 51 subjects per-formed 18 diverse activities of daily living.…”

Section: ) Wisdm Datasetmentioning

confidence: 99%

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A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

et al. 2020

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Research in the recognition of human activities of daily living has significantly improved using deep learning techniques. Traditional human activity recognition techniques often use handcrafted features from heuristic processes from single sensing modality. The development of deep learning techniques has addressed most of these problems by the automatic feature extraction from multimodal sensing devices to recognise activities accurately. In this paper, we propose a deep learning multi-channel architecture using a combination of convolutional neural network (CNN) and Bidirectional long short-term memory (BLSTM). The advantage of this model is that the CNN layers perform direct mapping and abstract representation of raw sensor inputs for feature extraction at different resolutions. The BLSTM layer takes full advantage of the forward and backward sequences to improve the extracted features for activity recognition significantly. We evaluate the proposed model on two publicly available datasets. The experimental results show that the proposed model performed considerably better than our baseline models and other models using the same datasets. It also demonstrates the suitability of the proposed model on multimodal sensing devices for enhanced human activity recognition.

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Section: ) Wisdm Datasetmentioning

confidence: 99%

“…The WISDM dataset [11], [12] is a unique example of multiple and complex activities, for example, brushing teeth, eating chip, eating sandwich amongst a set of 18 activities. These activities were the result of the large variability in movements beyond the normal set of activities which primarily are simple exercises.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

et al. 2020

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“…Recently cross-device interaction with wearables and other smart devices is studied in many areas. Weiss et al used smartphone and smartwatch to measure biometrics using activities of daily living (ADL) [8]. Bi et al studied identifying pen-holding gestures with a smartwatch [11].…”

Section: Emerging Technology For Wearablesmentioning

confidence: 99%

Collocated Wearable Interaction for Audio Book Application on Smartwatch and Hearables

Yoon¹,

Son²

2020

J Multimed Inf Syst

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This paper proposes a wearable audio book application using two wearable devices, a smartwatch and a hearables. We review requirements of what could be a killer wearable application and design our application based on these elicited requirements. To distinguish our application, we present 7 scenarios and introduce several wearable interaction modalities. To show feasibility of our approach, we design and implement our proof-of-concept prototype on Android emulator as well as on a commercial smartwatch. We thoroughly address how different interaction modalities are designed and implemented in the Android platform. Lastly, we show latency of the multi-modal and alternative interaction modalities that can be gracefully handled in wearable audio application use cases.

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“…Furthermore, ambient sensors may violate privacy. However, plenty of the existing HAR techniques are based on the data collected by ambient and smartphone sensors [ 13 , 14 , 15 , 16 ]. PAMAP2 is the dataset studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition

Abbaspour

Fotouhi

Sedaghatbaf

et al. 2020

Sensors

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Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

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Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living

Cited by 284 publications

References 32 publications

A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

Collocated Wearable Interaction for Audio Book Application on Smartwatch and Hearables

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition

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