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arxiv: 2510.23156 · v2 · submitted 2025-10-27 · 💻 cs.LG · cs.AI

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks

Koki Shibata , Tianheng Ling , Chao Qian , Tomokazu Matsui , Hirohiko Suwa , Keiichi Yasumoto , Gregor Schiele This is my paper

Pith reviewed 2026-05-18 04:01 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords vibration-based gesture recognitionFPGA implementation1D convolutional networksenergy-efficient inferenceraw waveform processingsmart home interfacesquantized neural networksedge deployment
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The pith

Compact 1D convolutional networks on low-power FPGAs enable accurate swipe gesture recognition from raw table vibrations at under 10 ms latency and 1.2 mJ energy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper demonstrates that vibration signals from everyday furniture can support gesture recognition without heavy preprocessing or large models. By feeding raw waveforms directly into small 1D-CNN and 1D-SepCNN architectures, the approach cuts input size by 21 times while preserving accuracy. Integer quantization to 6 or 8 bits combined with automated FPGA mapping and ping-pong buffering produces models that run on a Spartan-7 FPGA at 0.97 or 0.95 average accuracy across users. Both versions stay below 1.2 mJ per inference and deliver more than 50 times speedup over CPU execution. A hardware-aware search selects configurations that meet latency, energy, and memory limits for practical edge deployment.

Core claim

By replacing spectral preprocessing with raw waveform input, designing compact 1D-CNN and 1D-SepCNN models, applying integer-only quantization, and using automated RTL generation with a ping-pong buffer, the system achieves 0.970 accuracy at 9.22 ms latency for the 6-bit 1D-CNN or 0.949 accuracy at 6.83 ms for the 8-bit 1D-SepCNN on ordinary tables, with both consuming under 1.2 mJ per inference under person-specific data splitting.

What carries the argument

Lightweight 1D convolutional and separable convolutional networks that process raw vibration waveforms, quantized to 6-8 bits and mapped to FPGA via automated RTL generation and hardware-aware search.

If this is right

  • Gesture recognition becomes feasible on low-cost FPGAs without dedicated high-performance hardware or complex on-board signal processing.
  • Raw waveform input reduces data volume enough to fit real-time inference within tight FPGA memory and power budgets.
  • Quantized models deliver over 53 times CPU speedup while keeping energy low enough for always-on operation.
  • Hardware-aware model search balances accuracy against deployability constraints for practical edge devices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same raw-waveform and quantization approach could apply to other surface-vibration tasks such as object detection or knock recognition on furniture.
  • Low energy per inference opens the possibility of combining vibration sensing with energy-harvesting elements for battery-free smart furniture.
  • Eliminating spectral preprocessing may simplify integration with additional sensors for multi-modal home interfaces.

Load-bearing premise

The two swipe-direction datasets collected on ordinary tables with multiple users capture enough real-world variation in surfaces, user behavior, and noise that the reported accuracy will hold without extra on-device preprocessing or retraining.

What would settle it

Deploy the system on a new set of furniture surfaces and users in noisy environments and measure whether accuracy falls below 0.90 or energy per inference exceeds 2 mJ.

Figures

Figures reproduced from arXiv: 2510.23156 by Chao Qian, Gregor Schiele, Hirohiko Suwa, Keiichi Yasumoto, Koki Shibata, Tianheng Ling, Tomokazu Matsui.

Figure 1
Figure 1. Figure 1: Illustration of Vibration-Sensing Setup for Swipe [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Schematic diagram of an 1D-CNN accelerator [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: SepConv1DBN execution without and with ping-pong buffering between the depthwise and pointwise stages integrates a hardware-aware Optuna-based search framework to form a unified deployment flow from QAT to deployable FPGA implementations. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the deployment workflow, modified from [22] [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Confusion matrices of 1D-SepCNN models from Table III across data splitting methods. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition. While prior studies demonstrated feasibility, they often rely on complex preprocessing and large Neural Networks (NNs) requiring costly high-performance hardware, resulting in high energy usage and limited real-world deployability. This study proposes an energy-efficient solution deploying compact NNs on low-power Field-Programmable Gate Arrays (FPGAs) to enable real-time gesture recognition with competitive accuracy. We adopt a series of optimizations: (1) We replace complex spectral preprocessing with raw waveform input, eliminating complex on-board preprocessing while reducing input size by 21x without sacrificing accuracy. (2) We design two lightweight architectures (1D-CNN and 1D-SepCNN) tailored for embedded FPGAs, reducing parameters from 369 million to as few as 216 while maintaining comparable accuracy. (3) With integer-only quantization and automated RTL generation, we achieve seamless FPGA deployment. A ping-pong buffering mechanism in 1D-SepCNN further improves deployability under tight memory constraints. (4) We extend a hardware-aware search framework to support constraint-driven model configuration selection, considering accuracy, deployability, latency, and energy consumption. Evaluated on two swipe-direction datasets with multiple users and ordinary tables, our approach achieves low-latency, energy-efficient inference on the AMD Spartan-7 XC7S25 FPGA. Under the PS data splitting setting, the selected 6-bit 1D-CNN reaches 0.970 average accuracy across users with 9.22 ms latency. The chosen 8-bit 1D-SepCNN further reduces latency to 6.83 ms (over 53x CPU speedup) with slightly lower accuracy (0.949). Both consume under 1.2 mJ per inference, demonstrating suitability for long-term edge operation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes optimizations for vibration-based gesture recognition on everyday furniture, including raw waveform inputs (21x size reduction), lightweight 1D-CNN and 1D-SepCNN architectures (down to 216 parameters), integer quantization, and FPGA deployment on AMD Spartan-7 XC7S25. Under person-specific (PS) splitting on two swipe-direction datasets, it reports 0.970 average accuracy and 9.22 ms latency for the 6-bit 1D-CNN, and 0.949 accuracy with 6.83 ms latency (53x CPU speedup) for the 8-bit 1D-SepCNN, both under 1.2 mJ per inference.

Significance. If the results hold under broader conditions, the work demonstrates a practical path to low-power, real-time gesture interfaces on common surfaces without complex preprocessing or high-end hardware. Strengths include the concrete empirical metrics on held-out user splits, the parameter reduction, and the automated RTL generation for FPGA deployment. These address deployability gaps in prior vibration sensing work.

major comments (2)
  1. [Abstract / Experimental results] Abstract and results on PS splitting: The central claim of competitive accuracy (0.970 and 0.949) and real-world suitability for everyday furniture lacks any baseline comparisons to prior vibration-based methods or standard classifiers. This makes it difficult to assess whether the reported figures represent an advance or are simply consistent with simpler approaches under the narrow conditions tested.
  2. [Evaluation / Datasets] Dataset description and evaluation setup: The evaluation relies exclusively on two swipe-direction datasets collected on ordinary tables with multiple users. Vibration propagation is known to vary strongly with material damping, resonance, and environmental noise (e.g., footsteps or HVAC), yet no cross-surface testing or additive noise experiments are described. This assumption is load-bearing for the deployability claim on 'everyday furniture'.
minor comments (2)
  1. [Results] The manuscript does not report error bars, standard deviations across users, or statistical tests for the average accuracies, which would increase confidence in the 0.970 and 0.949 figures.
  2. [Abstract / Conclusion] No mention of code or data release, which would support reproducibility of the hardware-aware search and quantization steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below with honest clarifications and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Experimental results] Abstract and results on PS splitting: The central claim of competitive accuracy (0.970 and 0.949) and real-world suitability for everyday furniture lacks any baseline comparisons to prior vibration-based methods or standard classifiers. This makes it difficult to assess whether the reported figures represent an advance or are simply consistent with simpler approaches under the narrow conditions tested.

    Authors: We agree that explicit baseline comparisons would help readers better evaluate the advance. Our primary contributions center on the end-to-end optimizations—raw waveform input (21x size reduction), lightweight 1D-CNN/1D-SepCNN (down to 216 parameters), integer quantization, and hardware-aware search for Spartan-7 deployment—yielding concrete latency (<10 ms) and energy (<1.2 mJ) metrics that prior vibration-sensing papers rarely report together. Direct accuracy comparisons are complicated by differing datasets and hardware across prior work. In the revision we will add a table comparing our models against standard classifiers (SVM, Random Forest, and a non-optimized MLP) on the same datasets and PS splits to show that the reported accuracies are achieved with far lower complexity and embedded constraints. revision: yes

  2. Referee: [Evaluation / Datasets] Dataset description and evaluation setup: The evaluation relies exclusively on two swipe-direction datasets collected on ordinary tables with multiple users. Vibration propagation is known to vary strongly with material damping, resonance, and environmental noise (e.g., footsteps or HVAC), yet no cross-surface testing or additive noise experiments are described. This assumption is load-bearing for the deployability claim on 'everyday furniture'.

    Authors: We acknowledge that vibration signals are sensitive to surface material and noise, and our current results are limited to the two collected table-based datasets with multi-user PS splits. While these datasets already incorporate some real-world variability across users, we did not perform cross-surface or controlled noise-injection experiments. We will revise the manuscript to add an explicit Limitations section that discusses these factors, cites relevant literature on vibration propagation, and notes that the low-energy FPGA implementation facilitates rapid adaptation to new surfaces via retraining. This will temper the deployability claim without overstating generalizability. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical measurements on held-out splits with no self-referential derivations

full rationale

The paper reports an empirical implementation study: raw-waveform 1D-CNN and 1D-SepCNN models are trained and quantized, then deployed on FPGA with measured latency, energy, and accuracy on two swipe-direction datasets using PS user splits. No equations, uniqueness theorems, or predictions appear that reduce by construction to fitted inputs or prior self-citations; all performance numbers (0.970 accuracy, 9.22 ms latency, <1.2 mJ) are presented as direct experimental outcomes on held-out data rather than analytic derivations. The optimization steps (input reduction, parameter pruning, integer quantization) are design choices validated by measurement, not tautological redefinitions.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on empirical performance rather than theoretical derivation; the main unstated premises are that raw time-domain signals contain sufficient discriminative information and that the chosen datasets capture representative conditions.

free parameters (2)
  • quantization bit-width
    6-bit and 8-bit widths selected via hardware-aware search to trade accuracy against FPGA resource and latency constraints.
  • network depth and filter counts
    Architectural hyperparameters reduced from 369 million to 216 parameters through design choices and search.
axioms (1)
  • domain assumption Raw waveform input preserves sufficient information for accurate gesture classification without spectral preprocessing
    Claimed 21x input size reduction with no accuracy loss is presented as an empirical finding but functions as a foundational modeling choice.

pith-pipeline@v0.9.0 · 5906 in / 1450 out tokens · 31954 ms · 2026-05-18T04:01:35.345895+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards an End-To-End System for Real-Time Gesture Recognition from Surface Vibrations

    cs.AR 2026-05 unverdicted novelty 4.0

    An end-to-end piezoelectric sensor and compact 1D-CNN pipeline recognizes six gestures from desk vibrations with high accuracy, including strong user-independent results on data from 15 participants.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    Progress of advanced devices and Internet of Things systems as enabling technologies for smart homes and health care,

    Q. Shi, Y . Yang, Z. Sun, and C. Lee, “Progress of advanced devices and Internet of Things systems as enabling technologies for smart homes and health care,”ACS Materials Au, vol. 2, no. 4, pp. 394–435, 2022

    work page 2022

  2. [2]

    Development of an information projection interface using a projector–camera system,

    H. Goto, D. Takemura, Y . Kawasaki, and A. Nakamura, “Development of an information projection interface using a projector–camera system,” Electronics and Communications in Japan, vol. 96, pp. 70–81, 2013

    work page 2013

  3. [3]

    Developing a smart camera for gesture recogni- tion in HCI applications,

    Y . C. Ham and Y . Shi, “Developing a smart camera for gesture recogni- tion in HCI applications,” in2009 IEEE 13th International Symposium on Consumer Electronics, 2009, pp. 994–998

    work page 2009

  4. [4]

    Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects,

    F. Portet, M. Vacher, C. Golanski, C. Roux, and B. Meillon, “Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects,”Personal and Ubiquitous Computing, vol. 17, no. 1, pp. 127–144, 2013

    work page 2013

  5. [5]

    Room-localized speech activity detection in multi-microphone smart homes,

    P. Giannoulis, G. Potamianos, and P. Maragos, “Room-localized speech activity detection in multi-microphone smart homes,”EURASIP Journal on Audio, Speech, and Music Processing, vol. 2019, no. 1, p. 15, 2019

    work page 2019

  6. [6]

    Ultrasensitive capacitive sensor com- posed of nanostructured electrodes for human–machine interface,

    T. Li, V . Sakthivelpathi, Z. Qian, S.-J. Kahng, S. Ahn, A. B. Dichiara, K. Manohar, and J.-H. Chung, “Ultrasensitive capacitive sensor com- posed of nanostructured electrodes for human–machine interface,”Ad- vanced Materials Technologies, vol. 7, no. 10, p. 2101704, 2022

    work page 2022

  7. [7]

    Capricate: A fabrication pipeline to design and 3d print capacitive touch sensors for interactive objects,

    M. Schmitz, M. Khalilbeigi, M. Balwierz, R. Lissermann, M. M ¨uhlh¨auser, and J. Steimle, “Capricate: A fabrication pipeline to design and 3d print capacitive touch sensors for interactive objects,” in Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, 2015, pp. 253–258

    work page 2015

  8. [8]

    Smatable: A system to transform furniture into interface using vibration sensor,

    M. Yoshida, T. Matsui, T. Ishiyama, M. Fujimoto, H. Suwa, and K. Ya- sumoto, “Smatable: A system to transform furniture into interface using vibration sensor,” in2023 19th International Conference on Intelligent Environments (IE). IEEE, 2023, pp. 1–8

    work page 2023

  9. [9]

    Smatable: A vibration-based sensing method for making ordinary tables touch-interfaces,

    ——, “Smatable: A vibration-based sensing method for making ordinary tables touch-interfaces,”IEEE Access, vol. 11, pp. 142 611–142 627, 2023

    work page 2023

  10. [10]

    Designing for low-latency direct-touch input,

    A. Ng, J. Lepinski, D. Wigdor, S. Sanders, and P. Dietz, “Designing for low-latency direct-touch input,” inProceedings of the 25th annual ACM symposium on User interface software and technology, 2012, pp. 453–464

    work page 2012

  11. [11]

    Vibration-based pervasive computing and intelligent sensing,

    Y . Huang and K. Wu, “Vibration-based pervasive computing and intelligent sensing,”CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 4, pp. 219–239, 2020

    work page 2020

  12. [12]

    Surfacevibe: vibration-based tap & swipe tracking on ubiquitous surfaces,

    S. Pan, C. G. Ramirez, M. Mirshekari, J. Fagert, A. J. Chung, C. C. Hu, J. P. Shen, H. Y . Noh, and P. Zhang, “Surfacevibe: vibration-based tap & swipe tracking on ubiquitous surfaces,” inProceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2017, pp. 197–208

    work page 2017

  13. [13]

    Vibsense: Sensing touches on ubiquitous surfaces through vibration,

    J. Liu, Y . Chen, M. Gruteser, and Y . Wang, “Vibsense: Sensing touches on ubiquitous surfaces through vibration,” in14th Annual IEEE Interna- tional Conference on Sensing, Communication, and Networking. IEEE, 2017, pp. 1–9

    work page 2017

  14. [14]

    iWood: Makeable vibration sensor for inter- active plywood,

    T.-Y . Wu and X.-D. Yang, “iWood: Makeable vibration sensor for inter- active plywood,” inProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022, pp. 1–12

    work page 2022

  15. [15]

    Efficient Deep Learning: A survey on making Deep Learning models smaller, faster, and better,

    G. Menghani, “Efficient Deep Learning: A survey on making Deep Learning models smaller, faster, and better,”ACM Computing Surveys, vol. 55, no. 12, pp. 1–37, 2023

    work page 2023

  16. [16]

    A comparative study of time and frequency domain approaches to Deep Learning based speech enhancement,

    S. A. Nossier, J. Wall, M. Moniri, C. Glackin, and N. Cannings, “A comparative study of time and frequency domain approaches to Deep Learning based speech enhancement,” in2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020, pp. 1–8

    work page 2020

  17. [17]

    1-D Convolutional Neural Networks for signal processing applications,

    S. Kiranyaz, T. Ince, O. Abdeljaber, O. Avci, and M. Gabbouj, “1-D Convolutional Neural Networks for signal processing applications,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 8360–8364

    work page 2019

  18. [18]

    Performance comparison of 1D and 2D Convolutional Neural Networks for real-time classification of time series sensor data,

    S. M. Shahid, S. Ko, and S. Kwon, “Performance comparison of 1D and 2D Convolutional Neural Networks for real-time classification of time series sensor data,” in2022 International Conference on Information Networking (ICOIN). IEEE, 2022, pp. 507–511

    work page 2022

  19. [19]

    FPGA implementation for odor identification with Depthwise Separable Convolutional Neural Network,

    Z. Mo, D. Luo, T. Wen, Y . Cheng, and X. Li, “FPGA implementation for odor identification with Depthwise Separable Convolutional Neural Network,”Sensors, vol. 21, no. 3, p. 832, 2021

    work page 2021

  20. [20]

    Quantization and training of Neural Networks for efficient integer-arithmetic-only inference,

    B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. G. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of Neural Networks for efficient integer-arithmetic-only inference,”2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2704–2713, 2017

    work page 2018

  21. [21]

    Quantizing deep convolutional networks for efficient inference: A whitepaper

    R. Krishnamoorthi, “Quantizing Deep Convolutional Networks for effi- cient inference: A whitepaper,”ArXiv, vol. abs/1806.08342, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  22. [22]

    Automating versatile time-series analysis with tiny Transformers on embedded FPGAs,

    T. Ling, C. Qian, L. J. Haßler, and G. Schiele, “Automating versatile time-series analysis with tiny Transformers on embedded FPGAs,” in 2025 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), vol. 1, 2025, pp. 1–6

    work page 2025

  23. [23]

    TinyM2Net-v2: A compact low-power software hardware architecture for multimodal Deep Neural Networks,

    H.-A. Rashid, U. Kallakuri, and T. Mohsenin, “TinyM2Net-v2: A compact low-power software hardware architecture for multimodal Deep Neural Networks,”ACM Trans. Embed. Comput. Syst., May 2024

    work page 2024

  24. [24]

    BearingPGA-Net: A lightweight and deployable bearing fault diagnosis network via decoupled knowledge distillation and FPGA acceleration,

    J.-X. Liao, S.-L. Wei, C.-L. Xie, T. Zeng, J. Sun, S. Zhang, X. Zhang, and F.-L. Fan, “BearingPGA-Net: A lightweight and deployable bearing fault diagnosis network via decoupled knowledge distillation and FPGA acceleration,”IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–14, 2024

    work page 2024

  25. [25]

    ElasticAI: Creating and deploying energy-efficient Deep Learning accelerator for pervasive computing,

    C. Qian, T. Ling, and G. Schiele, “ElasticAI: Creating and deploying energy-efficient Deep Learning accelerator for pervasive computing,” in2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Work- shops). IEEE, 2023, pp. 297–299

    work page 2023

  26. [26]

    Flowprecision: Advancing FPGA-based real-time fluid flow estimation with linear quantization,

    T. Ling, J. Hoever, C. Qian, and G. Schiele, “Flowprecision: Advancing FPGA-based real-time fluid flow estimation with linear quantization,” in2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Work- shops). IEEE, 2024, pp. 733–738

    work page 2024

  27. [27]

    Integer-only quantized Transformers for embedded FPGA-based time-series forecasting in AIoT,

    T. Ling, C. Qian, and G. Schiele, “Integer-only quantized Transformers for embedded FPGA-based time-series forecasting in AIoT,” in2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT), 2024, pp. 38–44

    work page 2024