Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-21T13:24:18.423418Z
Paper Citation Record · LEDGER
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices
As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2602.06523.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
45 of 45 outbound references displayed
- verified exact2
- verified fuzzy43
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cde22672-33f8-439d-91dc-cd1420cb2526 · outbound
This paper cites Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.671953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9498ec5a-b683-4a54-8558-a2414b64b8ea · outbound
This paper cites TinyHAR: A lightweight deep learning model designed for human activity recognition.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices TinyHAR: A lightweight deep learning model designed for human activity recognition
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.603448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fc185d0e-8e38-4610-9dae-7bb2e1481d70 · outbound
This paper cites TinierHAR: Towards ultra-lightweight deep learning models for efficient human activity recognition on edge devices.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices TinierHAR: Towards ultra-lightweight deep learning models for efficient human activity recognition on edge devices
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.606540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 33ec12f3-b675-4e65-a880-245977ca3751 · outbound
This paper cites Ensembles of deep LSTM learners for activity recognition using wearables.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Ensembles of deep LSTM learners for activity recognition using wearables
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.678184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bdb6c978-7c28-4ac0-a3b4-47e1dffb653f · outbound
This paper cites Deep, convolutional, and recurrent models for human activity recognition using wearables.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Deep, convolutional, and recurrent models for human activity recognition using wearables
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.612273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6736bff6-cada-4641-a60f-4a6149558697 · outbound
This paper cites Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.553058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3db71fb2-2f4a-485b-a9b6-86695ad0e231 · outbound
This paper cites Deep learning for health informatics.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Deep learning for health informatics
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.579408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4c7086b7-4b5b-4976-a942-d113141e9965 · outbound
This paper cites Real-time human activity recognition from accelerometer data using convolutional neural networks.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Real-time human activity recognition from accelerometer data using convolutional neural networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.653786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1b1d8db9-6d1e-46c6-b459-5c6198d7e474 · outbound
This paper cites A tutorial on human activity recognition using body-worn inertial sensors.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices A tutorial on human activity recognition using body-worn inertial sensors
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.675036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 401fa3cc-9caa-45ba-99e7-41fb9fe2fff8 · outbound
This paper cites Activity recognition using cell phone accelerometers.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Activity recognition using cell phone accelerometers
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.623193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2a11d137-1ed1-429e-b195-65d1925a25ca · outbound
This paper cites A public domain dataset for human activity recognition using smartphones.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices A public domain dataset for human activity recognition using smartphones
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.549927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 494a6892-ec91-418b-867a-78ae23f598bc · outbound
This paper cites Mobile sensor data anonymization.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Mobile sensor data anonymization
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.647480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6b4d2074-c29b-439c-b929-10755bdc36bb · outbound
This paper cites The Opportunity challenge: A bench- mark database for on-body sensor-based activity recognition.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices The Opportunity challenge: A bench- mark database for on-body sensor-based activity recognition
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.567268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d9d4ced4-8f31-4906-842f-cf447e76648b · outbound
This paper cites Wearable activity tracking in car manufacturing.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Wearable activity tracking in car manufacturing
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.659944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e3a380d1-b649-4c3c-967f-5df5dc63685d · outbound
This paper cites Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.589387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ddf3e4a4-0970-4e79-85c7-66dd10ca0b63 · outbound
This paper cites Introducing a new benchmarked dataset for activity monitoring.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Introducing a new benchmarked dataset for activity monitoring
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.644064Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1402dc75-1e88-4b62-8863-189c4132ce3f · outbound
This paper cites UniMiB SHAR: A dataset for human activity recognition using acceleration data from smartphones.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices UniMiB SHAR: A dataset for human activity recognition using acceleration data from smartphones
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.556722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 427fb1c2-d498-4c38-80b2-72746c603f68 · outbound
This paper cites Decoupled weight decay regularization.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Decoupled weight decay regularization
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.586006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c6c99c7a-6f59-469c-878c-6c4f838d6995 · outbound
This paper cites Optuna: A next-generation hyperparameter optimization framework.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Optuna: A next-generation hyperparameter optimization framework
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.592483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f262a1ec-a948-4ac6-84c4-b638664df076 · outbound
This paper cites Long short-term memory.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Long short-term memory
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.656793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bdf4e2b2-f745-4439-8621-ca06df784d7e · outbound
This paper cites Speech recognition with deep recurrent neural networks.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Speech recognition with deep recurrent neural networks
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.634954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8135efac-ed01-4754-91c4-6bcc0ff14c88 · outbound
This paper cites Bidirectional recurrent neural net- works.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Bidirectional recurrent neural net- works
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.600652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8f602c62-e2a3-48d9-8c0d-ee75e7370e86 · outbound
This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Batch normalization: Accelerating deep network training by reducing internal covariate shift
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.629070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 20860215-9ce1-4aa9-9454-81bbdd713a0d · outbound
This paper cites Dropout: A simple way to prevent neural networks from over- fitting.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Dropout: A simple way to prevent neural networks from over- fitting
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.620265Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a2d77861-05b7-45ef-b1ef-512e2d912a96 · outbound
This paper cites Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.564164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c5a51850-17f7-4a9b-b093-fe65c747aeae · outbound
This paper cites Adam: A method for stochastic optimization.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Adam: A method for stochastic optimization
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.560494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4f610522-9c2e-4d33-a9b9-5f0dedb51e1c · outbound
This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Quantization and training of neural networks for efficient integer-arithmetic-only inference
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.641057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 68ac6d5b-c4a2-406d-9388-ff97b2d0096d · outbound
This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Quantizing deep convolutional networks for efficient inference: A whitepaper
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T13:25:11.508973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 23caeacc-18a4-4d82-9133-10b5e8f67e60 · outbound
This paper cites MLPerf Tiny benchmark.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices MLPerf Tiny benchmark
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.595136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7cb269d8-ba47-434f-bf04-3f54da635c28 · outbound
This paper cites Warden and D.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Warden and D
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.573340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8e18924d-de06-4ec4-8ddd-ee08cecbe926 · outbound
This paper cites MCUNet: Tiny deep learning on IoT devices.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices MCUNet: Tiny deep learning on IoT devices
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.650630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 31517eb1-44db-422b-ad85-3b327e80e564 · outbound
This paper cites CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T13:25:11.504220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bc0a288a-828b-4759-99fb-b3c4f5a569eb · outbound
This paper cites Attend and discriminate: Beyond the state-of-the-art for human activity recognition using wearable sensors.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Attend and discriminate: Beyond the state-of-the-art for human activity recognition using wearable sensors
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.609372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 88310963-17bd-42cc-813e-221ffc5370d3 · outbound
This paper cites GlobalFusion: A global attentional deep learning framework for multisensor information fusion.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices GlobalFusion: A global attentional deep learning framework for multisensor information fusion
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.631870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 706e3fde-7eca-4822-a98b-fc1a3c5b0ae5 · outbound
This paper cites AttnSense: Multi-level attention mechanism for multimodal human activity recognition.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices AttnSense: Multi-level attention mechanism for multimodal human activity recognition
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.614709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 14546774-b235-4f51-a420-431ff8454683 · outbound
This paper cites On attention models for human activ- ity recognition.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices On attention models for human activ- ity recognition
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.598040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c4d77d05-6eba-41b3-81d0-8e5201fd9fea · outbound
This paper cites MLP- HAR: Boosting performance and efficiency of HAR models on edge devices with purely fully connected layers.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices MLP- HAR: Boosting performance and efficiency of HAR models on edge devices with purely fully connected layers
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.669083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation db210e0d-63c6-4ae6-82d5-61484c1ccecf · outbound
This paper cites LHAR: Lightweight human activity recognition on knowledge distilla- tion.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices LHAR: Lightweight human activity recognition on knowledge distilla- tion
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.570466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation af3dbbf7-a409-42ae-b2a2-fbe74ba61eb1 · outbound
This paper cites A human activity recognition method based on lightweight feature extraction combined with pruned and quantized CNN for wearable device.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices A human activity recognition method based on lightweight feature extraction combined with pruned and quantized CNN for wearable device
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.617596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4f158e47-b766-4dcc-a4de-21a26fc10f90 · outbound
This paper cites Efficient human activity recognition using lookup table-based neural architecture search for mobile devices.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Efficient human activity recognition using lookup table-based neural architecture search for mobile devices
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.662917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0595527d-ecf1-412a-99b4-8103f836d8b7 · outbound
This paper cites Attention is all you need.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Attention is all you need
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.576532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 638f9af5-987d-4087-bad1-e1b4323b153a · outbound
This paper cites Are Transformers a useful tool for tiny devices in human activity recognition?.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Are Transformers a useful tool for tiny devices in human activity recognition?
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.638057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6d67109d-4bff-487c-865c-b850d8552885 · outbound
This paper cites Improv- ing deep learning for HAR with shallow LSTMs.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Improv- ing deep learning for HAR with shallow LSTMs
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.666025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 271a8f43-5182-4af6-b484-01eb72121e62 · outbound
This paper cites A lightweight framework for human activity recognition on wearable devices.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices A lightweight framework for human activity recognition on wearable devices
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.582749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ac786ac6-0c6f-4a3d-8688-5d596ff7004a · outbound
This paper cites Human activity recognition with smart- phone sensors using deep learning neural networks.
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices Human activity recognition with smart- phone sensors using deep learning neural networks
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T13:25:11.626132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.