Binarized-BLSTM-RNN based Human Activity Recognition

Edel, Marcus; Köppe, Enrico

doi:10.1109/ipin.2016.7743581

Cited by 104 publications

(60 citation statements)

References 12 publications

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“…(Inoue et al, 2016) investigated several model parameters first and then proposed a relatively good model which can perform HAR with high throughput. (Edel and Köppe, 2016) proposed a binarized-BLSTM-RNN model, in which the weight parameters, input, and output of all hidden layers are all binary values. The main line of RNN based HAR models is dealing with resource-constrained environments while still achieve good performance.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Sensor Modality Deep Model Application Dataset (Almaslukh et al, 2017) Body-worn SAE ADL D03 (Alsheikh et al, 2016) Body-worn RBM ADL, factory, Parkinson D02, D06, D14 Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al, 2016b) Body-worn CNN ADL D06 (Cheng and Scotland, 2017) Body-worn DNN Parkinson Self (Edel and Köppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and Hu, 2014) Object, ambient DBN ADL Self (Gjoreski et al, 2016) Body-worn CNN ADL Self, D01 (Guan and Ploetz, 2017) Body-worn, object, ambient RNN ADL, smart home D01, D02, D04 (Ha et al, 2015) Body-worn CNN Factory, health D02, D13 (Ha and Choi, 2016) Body-worn CNN ADL, health D13 (Hammerla et al, 2015) Body-worn RBM Parkinson Self (Hammerla et al, 2016) Body-worn, object, ambient DNN, CNN, RNN ADL, smart home, gait D01, D04, D14 (Hannink et al, 2017) Body-worn CNN Gait Self (Hayashi et al, 2015) Body-worn, ambient RBM ADL, smart home D16 (Inoue et al, 2016) Body-worn RNN ADL D16 (Jiang and Yin, 2015) Body-worn CNN ADL D03, D05, D11 (Khan et al, 2017) Ambient CNN Respiration Self (Kim and Toomajian, 2016) Ambient CNN Hand gesture Self (Kim and Li, 2017) Body-worn CNN ADL Self Body-worn, ambient RBM ADL, emotion Self Ambient RBM ADL Self (Lee et al, 2017) Body-worn CNN ADL Self (Li et al, 2016a) Object RBM Patient resuscitation Self (Li et al, 2016b) Object CNN Patient resuscitation Self (Li et al, 2014) Body-worn SAE ADL D03 Body-worn CNN, RBM ADL Self (Mohammed and Tashev, 2017) Body-worn CNN ADL, gesture Self (Morales and Roggen, 2016) Body-worn CNN ADL, smart home D01, D02 (Murad and Pyun, 2017) Body-worn RNN ADL, smart home D01, D02, D05, D14 (Ordóñez and Roggen, 2016) Body-worn CNN, RNN ADL, gesture, posture, factory D01, D02 (Panwar et al, 2017) Body-worn CNN ADL Self (Plötz et al, 2011) Body-worn, object RBM ADL, food preparation, factory D01, D02, D08, D14…”

Section: Literaturementioning

confidence: 99%

See 1 more Smart Citation

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,555

927

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Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• We survey deep learning based HAR in sensor modality, deep model, and application.• We comprehensively discuss the insights of deep learning models for HAR tasks.• We extensively investigate why deep learning can improve the performance of HAR.• We also summarize the public HAR datasets frequently used for research purpose.• We present some grand challenges and feasible solutions for deep learning based HAR. ABSTRACTSensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,555

927

View full text Add to dashboard Cite

show abstract

“…Ordóñez and Roggen architect an advanced ConvLSTM to fuse data gathered from multiple sensors and perform activity recognition [112]. By leveraging CNN and LSTM structures, ConvLSTMs can automatically compress spatio-temporal sensor data into low-dimensional [236] Mobile ear Edge-based CNN Jindal [237] Heart rate prediction Cloud-based DBN Kim et al [238] Cytopathology classification Cloud-based CNN Sathyanarayana et al [239] Sleep quality prediction Cloud-based MLP, CNN, LSTM Li and Trocan [240] Health conditions analysis Cloud-based Stacked AE Hosseini et al [241] Epileptogenicity localisation Cloud-based CNN Stamate et al [242] Parkinson's symptoms management Cloud-based MLP Quisel et al [243] Mobile health data analysis Cloud-based CNN, RNN Khan et al [244] Respiration [250] Facial recognition Cloud-based CNN Wu et al [291] Mobile visual search Edge-based CNN Rao et al [251] Mobile augmented reality Edge-based CNN Ohara et al [290] WiFi-driven indoor change detection Cloud-based CNN,LSTM Zeng et al [252] Activity recognition Cloud-based CNN, RBM Almaslukh et al [253] Activity recognition Cloud-based AE Li et al [254] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [255] Smart watch-based activity recognition Edge-based RBM Antreas and Angelov [256] Mobile surveillance system Edge-based & Cloud based CNN Ordóñez and Roggen [112] Activity recognition Cloud-based ConvLSTM Wang et al [257] Gesture recognition Edge-based CNN, RNN Gao et al [258] Eating detection Cloud-based DBM, MLP Zhu et al [259] User energy expenditure estimation Cloud-based CNN, MLP Sundsøy et al [260] Individual income classification Cloud-based MLP Chen and Xue [261] Activity recognition Cloud-based CNN Ha and Choi [262] Activity recognition Cloud-based CNN Edel and Köppe [263] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al…”

Section: Mobilementioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,383

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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“…Second, DL models can be reused for similar tasks, which makes HAR model construction more efficient. Different DL models such as deep neural networks [26,27], convolutional neural networks [10,28], autoencoders [11,29], restricted Boltzmann machines [12,30], and recurrent neural networks [31,32] have been applied in HAR. We refer readers to [8] for more details on DL-based HAR.…”

Section: Human Activity Recognitionmentioning

confidence: 99%

Few-shot learning-based human activity recognition

Feng

Duarte

2019

Expert Systems with Applications

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Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-shot learning method for wearable sensor based human activity recognition, a technique that seeks high-level human activity knowledge from low-level sensor inputs. Due to the high costs to obtain human generated activity data and the ubiquitous similarities between activity modes, it can be more efficient to borrow information from existing activity recognition models than to collect more data to train a new model from scratch when only a few data are available for model training. The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer. In order to alleviate negative transfer, we propose a metric to measure cross-domain class-wise relevance so that knowledge of higher relevance is assigned larger weights during knowledge transfer. Promising results in extensive experiments show the advantages of the proposed approach.

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Binarized-BLSTM-RNN based Human Activity Recognition

Cited by 104 publications

References 12 publications

Deep learning for sensor-based activity recognition: A survey

Deep learning for sensor-based activity recognition: A survey

Deep Learning in Mobile and Wireless Networking: A Survey

Few-shot learning-based human activity recognition

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