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Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

https://doi.org/10.1145/3542819
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine
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Energy-efficient deep learning inference on edge devices
inferredcites →https://doi.org/10.1016/j.neucom.2015.07.085openalex-snapshot
Transition-Aware Human Activity Recognition Using Smartphones
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A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
inferredcites →https://doi.org/10.1016/j.procs.2020.03.289openalex-snapshot
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2021.3122298openalex-snapshot
HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition
inferredcites →https://doi.org/10.1109/cbd.2013.19openalex-snapshot
Human Activity Recognition Model Based on Decision Tree
inferredcites →https://doi.org/10.1109/codesisss.2015.7331375openalex-snapshot
Big/little deep neural network for ultra low power inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00171openalex-snapshot
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00843openalex-snapshot
HydraNets: Specialized Dynamic Architectures for Efficient Inference
inferredcites →https://doi.org/10.1109/cvpr.2019.00881openalex-snapshot
HAQ: Hardware-Aware Automated Quantization With Mixed Precision
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00242openalex-snapshot
Rethinking Differentiable Search for Mixed-Precision Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01298openalex-snapshot
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
inferredcites →https://doi.org/10.1109/dac18074.2021.9586187openalex-snapshot
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
inferredcites →https://doi.org/10.1109/hotchips.2015.7477325openalex-snapshot
PULP: A parallel ultra low power platform for next generation IoT applications
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207413openalex-snapshot
EvoQ: Mixed Precision Quantization of DNNs via Sensitivity Guided Evolutionary Search
inferredcites →https://doi.org/10.1109/islped52811.2021.9502494openalex-snapshot
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
inferredcites →https://doi.org/10.1109/jiot.2019.2920283openalex-snapshot
IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment
inferredcites →https://doi.org/10.1109/jproc.2019.2921977openalex-snapshot
Deep Learning With Edge Computing: A Review
inferredcites →https://doi.org/10.1109/jsen.2020.3015521openalex-snapshot
Layer-Wise Training Convolutional Neural Networks With Smaller Filters for Human Activity Recognition Using Wearable Sensors
inferredcites →https://doi.org/10.1109/les.2021.3065206openalex-snapshot
Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes
inferredcites →https://doi.org/10.1109/meco.2019.8760205openalex-snapshot
Open-Source RISC-V Processor IP Cores for FPGAs — Overview and Evaluation
inferredcites →https://doi.org/10.1109/tcsi.2020.3011984openalex-snapshot
A Partially Binarized Hybrid Neural Network System for Low-Power and Resource Constrained Human Activity Recognition
inferredcites →https://doi.org/10.1109/tip.2020.3018269openalex-snapshot
Learning Layer-Skippable Inference Network
inferredcites →https://doi.org/10.1109/tpami.2021.3117837openalex-snapshot
Dynamic Neural Networks: A Survey
inferredcites →https://doi.org/10.1145/1964897.1964918openalex-snapshot
Activity recognition using cell phone accelerometers
inferredcites →https://doi.org/10.1145/2905055.2905232openalex-snapshot
Performance Evaluation of Classifiers on WISDM Dataset for Human Activity Recognition
inferredcites →https://doi.org/10.1145/3126858.3126859openalex-snapshot
A Comparative Analysis of the Impact of Features on Human Activity Recognition with Smartphone Sensors
inferredcites →https://doi.org/10.1145/3218603.3218611openalex-snapshot
Dynamic Bit-width Reconfiguration for Energy-Efficient Deep Learning Hardware
inferredcites →https://doi.org/10.1145/3387902.3394038openalex-snapshot
Enabling mixed-precision quantized neural networks in extreme-edge devices
inferredcites →https://doi.org/10.1145/3457388.3458656openalex-snapshot
Ultra-compact binary neural networks for human activity recognition on RISC-V processors
inferredcites →https://doi.org/10.1177/0020294018813692openalex-snapshot
Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm
inferredcites →https://doi.org/10.3390/app7101101openalex-snapshot
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
inferredcites →https://doi.org/10.3390/electronics9020337openalex-snapshot
Sequence-To-Sequence Neural Networks Inference on Embedded Processors Using Dynamic Beam Search
inferredcites →https://doi.org/10.3390/s151229858openalex-snapshot
Physical Human Activity Recognition Using Wearable Sensors
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1706.04599openalex-snapshot
On Calibration of Modern Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1805.06085openalex-snapshot
PACT: Parameterized Clipping Activation for Quantized Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1810.05331openalex-snapshot
Dynamic Channel Pruning: Feature Boosting and Suppression
inferredcites →https://doi.org/10.48550/arxiv.1812.08928openalex-snapshot
Slimmable Neural Networks
inferredcites →https://doi.org/10.5220/0005792401430151openalex-snapshot
The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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On-Device Learning for Human Activity Recognition on Low-Power Microcontrollers
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Hardware-constrained deployment of an INT8 MobileNetV1 for single-image human activity recognition on an STM32F407 microcontroller
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A systematic literature review on human activity recognition using smart devices: advances, challenges, and future directions
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Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system
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Multi-modal hybrid hierarchical classification approach with transformers to enhance complex human activity recognition
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Composite human activity recognition utilizing knowledge distillation and sensor fusion focusing on resource constrained microcontrollers
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AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design
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