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Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

Dimitri Kraft, Karthik Srinivasan, Gerald Bieber
<span title="2020-12-02">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ccathu4omjd55by7ulzsocssly" style="color: black;">Technologies</a> </i> &nbsp;
The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly.  ...  Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems.  ...  To train a recurrent neural network, the back-propagation through time algorithm is used. Recurrent neural networks emerged in 1986 [44] and underlie various advances.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/technologies8040072">doi:10.3390/technologies8040072</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l5bhldeubrf7rgrhmtqh64tzcm">fatcat:l5bhldeubrf7rgrhmtqh64tzcm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201205162211/https://res.mdpi.com/d_attachment/technologies/technologies-08-00072/article_deploy/technologies-08-00072.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/c7/2ac767f6b5da390dbf08530555c152ae04bd81f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/technologies8040072"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

Sakorn Mekruksavanich, Anuchit Jitpattanakul, Kanokwan Sitthithakerngkiet, Phichai Youplao, Preecha Yupapin
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
To address the problems pertaining to complex HAR, a deep neural network composed of convolutional layers and residual networks was developed in this work.  ...  Smart mobile devices are being widely used to identify and track human behaviors in simple and complex daily activities.  ...  In particular, convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have demonstrated impressive data capture and fitting abilities in a variety of applications [16] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3174124">doi:10.1109/access.2022.3174124</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7pesgbxcd5a5rbhh4ptriccxty">fatcat:7pesgbxcd5a5rbhh4ptriccxty</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220512234059/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09771436.pdf?tp=&amp;arnumber=9771436&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/25/b0/25b0f9bf2b0cf8f7f0fc3e1eacaf58950c15360d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3174124"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements

Sakorn Mekruksavanich, Anuchit Jitpattanakul
<span title="2022-04-18">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Wearable technology has advanced significantly and is now used in various entertainment and business contexts.  ...  This research used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification.  ...  Convolutional Neural Network Table 2 . 2 Characteristics of the selected activity-based datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22083094">doi:10.3390/s22083094</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35459078">pmid:35459078</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9031464/">pmcid:PMC9031464</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/togfrups45glxlu2naf26ow5am">fatcat:togfrups45glxlu2naf26ow5am</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220422004326/https://mdpi-res.com/d_attachment/sensors/sensors-22-03094/article_deploy/sensors-22-03094-v2.pdf?version=1650354593" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/4e/2d4e409fb45231a85f4867951a6e53357fad03a2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22083094"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9031464" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Smartwatch User Interface Implementation Using CNN-Based Gesture Pattern Recognition

Min-Cheol Kwon, Geonuk Park, Sunwoong Choi
<span title="2018-09-07">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
In recent years, with an increase in the use of smartwatches among wearable devices, various applications for the device have been developed.  ...  By incorporating the convolution neural network (CNN) model, the proposed pattern recognition system has become more accurate than the existing model.  ...  We propose a method that classifies 10 gesture patterns using the convolution neural network (CNN) classification model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18092997">doi:10.3390/s18092997</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30205509">pmid:30205509</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6164391/">pmcid:PMC6164391</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pgtccnzsqfhopibuytdbjllkfq">fatcat:pgtccnzsqfhopibuytdbjllkfq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218104207/http://pdfs.semanticscholar.org/0ad5/6c9bbf0a8c827a23e4ea4d5400b032ad853f.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/d5/0ad56c9bbf0a8c827a23e4ea4d5400b032ad853f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18092997"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164391" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep Neural Networks [article]

Shayan Hassantabar, Joe Zhang, Hongxu Yin, Niraj K. Jha
<span title="2021-02-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The recent introduction of body-area networks consisting of a plethora of accurate sensors embedded in smartwatches and smartphones and deep neural networks (DNNs) points towards a possible solution.  ...  MHDeep uses eight different categories of data obtained from sensors integrated in a smartwatch and smartphone.  ...  from various patient cohorts and providing patient labels.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.10435v1">arXiv:2102.10435v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/twslp43gcbcxpp4mgqjtdrbgk4">fatcat:twslp43gcbcxpp4mgqjtdrbgk4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210227202906/https://arxiv.org/pdf/2102.10435v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/70/5e7044541a49b80bd85e8bcbd4a9740ec5a55fbc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.10435v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices

Hyeyeoun Joo, Hyejoo Kim, Jeh-Kwang Ryu, Semin Ryu, Kyoung-Min Lee, Seung-Chan Kim
<span title="2022-01-24">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vyslcn4ljzdq3jes5w7fln3qyu" style="color: black;">International Journal of Environmental Research and Public Health</a> </i> &nbsp;
The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed  ...  In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device.  ...  Acknowledgments: The authors would like to thank the reviewers for all of their constructive and insightful comments in relation to this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19031279">doi:10.3390/ijerph19031279</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35162308">pmid:35162308</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8835219/">pmcid:PMC8835219</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sg6237j7afepxosmqikiq2fhxe">fatcat:sg6237j7afepxosmqikiq2fhxe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220209205141/https://mdpi-res.com/d_attachment/ijerph/ijerph-19-01279/article_deploy/ijerph-19-01279.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/4c/e04c095b3a97c3d26ac0534e68f5a30112ca3351.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19031279"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835219" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

CNN-based Speed Detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors [article]

Venkata Devesh Reddy Seethi, Pratool Bharti
<span title="2020-06-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with  ...  In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly.  ...  The neural network architectures are used in applications relating to human activity recognition [28] , and predicting relative physical activity intensity of walking-related activities [29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.02348v1">arXiv:2006.02348v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xana27qfkvdprlj64gs7r7y2lq">fatcat:xana27qfkvdprlj64gs7r7y2lq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200903120134/https://arxiv.org/pdf/2006.02348v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/f4/83f46b733e3709868b927e53cd28f6579f85249e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.02348v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks

Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dsrvu6bllzai7oj3hktnc5yf4q" style="color: black;">Computer</a> </i> &nbsp;
Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications.  ...  However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored.  ...  Name Type of DL Architecture Application Techniques SparseSep 3 Convolutional neural network (CNN), deep neural network (DNN) Multiple layers Image classification, speaker identification, and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mc.2018.2381119">doi:10.1109/mc.2018.2381119</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yorsmxetvnap3eeqznlnd6fevi">fatcat:yorsmxetvnap3eeqznlnd6fevi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321190357/https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5057&amp;context=sis_research" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0d/a9/0da9b728ee4103d349b4dbfdc26c0ee3bdcdebab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mc.2018.2381119"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation

Md Abdullah Al Hafiz Khan, Nirmalya Roy, Archan Misra
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ws3vd5nlzvefte7qzakqlvjur4" style="color: black;">2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)</a> </i> &nbsp;
tuned to the properties of convolutional neural networks (CNNs).  ...  competitive shallow and deep classifiers) when even a modest amount of labeled training data is available.  ...  In this paper, we propose a framework for scalable human activity recognition, based on a deep convolutional neural network (CNN) model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/percom.2018.8444585">doi:10.1109/percom.2018.8444585</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/percom/KhanRM18.html">dblp:conf/percom/KhanRM18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eckofi5jonct7nebnxygunxb3a">fatcat:eckofi5jonct7nebnxygunxb3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104175000/https://mdsoar.org/bitstream/handle/11603/11220/IEEE-PerCom_2018_Hafiz.pdf;jsessionid=394DB2ED756ED32D0B174760005DB179?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/9c/e09c46a9277e4a557ea7ab16f77aeda241c1f350.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/percom.2018.8444585"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Taeho Hur, Jaehun Bang, Thien Huynh-The, Jongwon Lee, Jee-In Kim, Sungyoung Lee
<span title="2018-11-13">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC)  ...  We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18113910">doi:10.3390/s18113910</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/557hm24lnjgozcwip7aom6tpiy">fatcat:557hm24lnjgozcwip7aom6tpiy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190307053629/http://pdfs.semanticscholar.org/db6e/92ded67cfb9d29c5f9bf6525af5a7d026e17.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/db/6e/db6e92ded67cfb9d29c5f9bf6525af5a7d026e17.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18113910"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Evaluation of Deep Learning Models for Smoking Recognition with Smartwatch and Smartphone Sensors

Yasemin AKAN, Sümeyye AĞAÇ, Özlem DURMAZ İNCEL
<span title="2021-08-03">2021</span> <i title="Balkan Journal of Electrical &amp; Computer Engineering (BAJECE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pfzvpr7eu5gxvafao5jgxa5she" style="color: black;">Balkan Journal of Electrical and Computer Engineering</a> </i> &nbsp;
(LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).  ...  Smartwatches and smartphones are extensively used in human activity recognition, particularly for step counting and daily sports applications, thanks to the motion sensors integrated into these devices  ...  We thank Beyza Cankuş for her contributions in the design of experiments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17694/bajece.827342">doi:10.17694/bajece.827342</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hzsz3phynzer7omkxje2hoddvm">fatcat:hzsz3phynzer7omkxje2hoddvm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211102140954/https://dergipark.org.tr/en/download/article-file/1402182" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f4/06/f406eeb4dc5e9c823216e8888f19a67e075d0244.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17694/bajece.827342"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning

Andrea Soro, Gino Brunner, Simon Tanner, Roger Wattenhofer
<span title="2019-02-10">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
We additionallyshow that the same neural network used for exercise recognition can also be used in repetitioncounting.  ...  Activity recognition using off-the-shelf smartwatches is an important problem in humanactivity recognition.  ...  Convolutional Neural Networks (CNNs), variants of Recurrent Neural Networks (RNNs), and combinations of both methods have been shown to work very well on a range of HAR benchmark tasks [21] [22] [23]  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19030714">doi:10.3390/s19030714</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vail57x7wbewhm7y7coa4rofeu">fatcat:vail57x7wbewhm7y7coa4rofeu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503130020/https://res.mdpi.com/sensors/sensors-19-00714/article_deploy/sensors-19-00714-v2.pdf?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8b/b4/8bb4aea0a5094a8efb7494759a53363a3aafbc54.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19030714"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

Mazin Alshamrani
<span title="">2021</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
In this research, a novel approach to designing and using a deep neural network for stress detection is presented.  ...  Finally, two network types with proven capabilities of processing time series data are examined in detail: a fully convolutional network (FCN) and a ResNet deep learning model.  ...  APPLICATION OF THE FULLY CONVOLUTIONAL NETWORK TO STRESS DETECTION TASKS To design a model useful to a broad audience and applicable to almost any interested party, the training processes of neural network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120745">doi:10.14569/ijacsa.2021.0120745</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ipnzosypgfd6vi4lddozufonna">fatcat:ipnzosypgfd6vi4lddozufonna</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210805005955/https://thesai.org/Downloads/Volume12No7/Paper_45-An_Advanced_Stress_Detection_Approach.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/b9/2db924c0805f25f07982da833aea3b3c986adc1d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120745"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Explainability via Interactivity? Supporting Nonexperts' Sensemaking of Pretrained CNN by Interacting with Their Daily Surroundings [article]

Chao Wang, Pengcheng An
<span title="2021-05-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present a mobile application to support nonexperts to interactively make sense of Convolutional Neural Networks (CNN); it allows users to play with a pretrained CNN by taking pictures of their surrounding  ...  We use an up-to-date XAI technique (Class Activation Map) to intuitively visualize the model's decision (the most important image regions that lead to a certain result).  ...  INTRODUCTION With its advantages of solving computer vision problems such as object detection or image classification, Convolutional Neural Networks (CNN) are wildly applied in various domains e.g., autonomous  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.01996v1">arXiv:2107.01996v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ffmec47tpnebjf7uwrreq4cx7q">fatcat:ffmec47tpnebjf7uwrreq4cx7q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210712202652/https://arxiv.org/ftp/arxiv/papers/2107/2107.01996.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7b/2d/7b2d1c94a072855304ee4bdd5bc49aa6e83fc3e1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.01996v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

iWash: A Smartwatch Handwashing Quality Assessment and Reminder System with Real-time Feedback in the Context of Infectious Disease [article]

Sirat Samyoun, Sudipta Saha Shubha, Md Abu Sayeed Mondol, John A. Stankovic
<span title="2020-09-22">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present iWash, a comprehensive system for quality assessment and context-aware reminder for handwashing with real-time feedback using smartwatches. iWash is a hybrid deep neural network based system  ...  user and the smartwatch that minimizes the risk of germ transmission.  ...  The best classification results were obtained using a neural network based solution and a k-nearest neighbor based solution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.10317v1">arXiv:2009.10317v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xarxl7q7kbbezlm4l42ooiqajq">fatcat:xarxl7q7kbbezlm4l42ooiqajq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200924001523/https://arxiv.org/pdf/2009.10317v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.10317v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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