Fall Detection Using Spiking Neural Networks

Research implementation of fall detection on resource-constrained microcontrollers using Spiking Neural Networks (SNNs).

📋 Overview

This repository contains the complete pipeline for training, converting, and deploying fall detection models on microcontrollers:

• Preprocessing: KFall and SisFall dataset fusion with temporal segmentation
• Baseline Models: CNN-only, CNN-LSTM ensemble comparison
• Microcontroller-Optimized CNN: 41K parameters, fits on Arduino
• SNN Conversion: Event-driven spiking neural networks for low-power inference
• Hardware Deployment: Arduino Nano 33 BLE Sense implementation

Key Finding: Temporal modeling (LSTM/LMU) provides no accuracy benefit for fall detection. CNN-only outperforms CNN-LSTM (89.98% vs 79.96%), validating simple feedforward SNNs for neuromorphic deployment.

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🚀 Quick Start

Prerequisites

• Python 3.13+
• UV package manager
• Kaggle account (for dataset download)

Installation

# Clone repository
git clone https://github.com/yourusername/fall-detection.git
cd fall-detection

# Install dependencies with UV
uv sync

# Setup Kaggle API credentials (one-time)
# 1. Get API token: https://www.kaggle.com/settings
# 2. Download kaggle.json
# 3. Place in ~/.kaggle/
mkdir -p ~/.kaggle
mv ~/Downloads/kaggle.json ~/.kaggle/
chmod 600 ~/.kaggle/kaggle.json

Download Datasets

# Download both KFall and SisFall (~1.2 GB total)
python scripts/download_datasets.py

# Or verify existing data
python scripts/download_datasets.py --verify-only

Explore Data

# Start Jupyter
jupyter notebook

# Open notebooks/01_data_exploration.ipynb

---

📊 Datasets

SisFall

• Source: Kaggle
• Subjects: 38 (23 young adults, 15 elderly)
• Activities: 19 ADLs + 15 fall types
• Sampling: 200 Hz, 9-axis IMU (ADXL345 + ITG3200)
• Files: 4,506 recordings
• Size: ~720 MB

KFall

• Source: Kaggle
• Subjects: 32
• Activities: Fall types (T28-T34), Stumble (T10)
• Sampling: 100 Hz (upsampled to 200 Hz)
• Files: 5,075 recordings
• Size: ~468 MB

Citation:

@article{sucerquia2017sisfall,
  title={SisFall: A fall and movement dataset},
  author={Sucerquia, Angela and L{\'o}pez, Jos{\'e} David and Vargas-Bonilla, Jes{\'u}s Francisco},
  journal={Sensors},
  volume={17},
  number={1},
  pages={198},
  year={2017}
}

---

🧪 Experimental Results

Model Comparison

Model	Accuracy	Std Dev	Parameters	Memory	Arduino?	Notes
CNN-only	89.98%	±0.42%	13.6M	54.5 MB	❌	Best baseline
CNN-LSTM ensemble	79.96%	±7.31%	14.0M	55.8 MB	❌	Unstable, 40% fold failures
CNN-LMU	89.97%	±0.29%	13.7M	54.8 MB	❌	Same as CNN-only
Micro-CNN	~87%*	TBD	41K	164 KB	✅	Deployable
Micro-SNN	~85%*	TBD	40K	55 KB	✅	Low-power

*Target accuracy (in progress)

Class-wise Performance (CNN-only, 6-class)

Class	Precision	Recall	F1-Score	Support
Walking	0.805	0.670	0.732	2,637
Jogging	0.789	0.644	0.709	1,860
Walking_stairs_updown	0.733	0.936	0.822	5,852
Stumble_while_walking	0.631	0.363	0.461	1,479
Fall_Initiation	0.961	0.733	0.832	1,649
Impact_Aftermath	0.927	0.980	0.953	3,255

Critical metric: Fall_Initiation recall = 73.3% (95.6% for best fold)

Key Findings

1. Temporal modeling provides no benefit

   • CNN-only: 89.98% ± 0.42%
   • CNN-LSTM: 79.96% ± 7.31% (unstable)
   • Falls are instantaneous events captured by spatial convolutions

2. LSTM training instability

   • 40% fold failure rate
   • Fall_Initiation recall: 46.4% (worst fold) vs 95.5% (best fold)
   • Caused by small batch size (32-64) + class imbalance

3. Microcontroller deployment is viable

   • 330x parameter reduction (13.6M → 41K)
   • <3% accuracy drop (target)
   • Fits on $35 Arduino Nano 33 BLE Sense

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📁 Repository Structure

fall-detection/
├── fall_detection/              # Python package
│   └── __init__.py
├── fall_detection_data/         # Datasets (gitignored)
│   ├── KFall/                   # KFall dataset
│   ├── SisFall/                 # SisFall dataset
│   ├── processed/               # Preprocessed data
│   │   ├── X_data_6class.npy   # 16,732 samples
│   │   └── y_labels_6class.npy # 6 classes
│   └── models/                  # Trained models
├── notebooks/                   # Jupyter notebooks
│   ├── 01_data_exploration.ipynb
│   ├── 02_preprocessing_6class.ipynb
│   ├── 03_train_cnn_only.ipynb
│   └── 04_results_analysis.ipynb
├── scripts/                     # Automation scripts
│   └── download_datasets.py     # Kaggle dataset downloader
├── Research/                    # Papers and references
├── pyproject.toml              # Project dependencies
├── uv.lock                     # Locked dependencies
└── README.md                   # This file

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🔬 Preprocessing Pipeline

6-Class Merged Dataset

Classes:

1. Walking
2. Jogging
3. Walking_stairs_updown
4. Stumble_while_walking
5. Fall_Initiation (pre-impact, <1s before fall)
6. Impact_Aftermath (merged: Impact + Aftermath due to insufficient Fall_Recovery data)

Why merge Impact + Aftermath?

• Fall_Recovery: Only 159 samples (0.94%) - insufficient for training
• Solution: Merge Impact (1,646) + Aftermath (1,609) = 3,255 samples

Processing Steps

1. Data Loading

   • SisFall: Convert from bits to physical units (g, °/s)
   • KFall: Upsample 100 Hz → 200 Hz (cubic spline)

2. Temporal Segmentation (Algorithm 1 from Jain & Semwal 2022)

   • Automatic fall detection using Y-axis acceleration variance
   • Extract: ADL (before), Fall_Initiation, Impact, Aftermath
   • Window: 200 samples (1 second @ 200 Hz)

3. Normalization

   • Per-dataset Z-score normalization
   • Dataset fusion
   • Final Z-score normalization

4. Output

   • X: (16,732, 200, 6) - samples × timesteps × features
   • y: (16,732,) - class labels
   • Features: [AccX, AccY, AccZ, GyrX, GyrY, GyrZ]

---

🎯 Reproducing Results

Step 1: Preprocess Data

# Run preprocessing notebook
jupyter notebook notebooks/02_preprocessing_6class.ipynb

# Expected output:
# - X_data_6class.npy (16,732 samples)
# - y_labels_6class.npy (6 classes)

Step 2: Train CNN-Only Baseline

# Run training notebook
jupyter notebook notebooks/03_train_cnn_only.ipynb

# Expected results:
# Accuracy: 89.98% ± 0.42%
# Fall_Initiation Recall: 95.6% (best fold)

Step 3: Train Micro-CNN (In Progress)

# Train microcontroller-optimized model
python scripts/train_micro_cnn.py

# Expected:
# Parameters: 41,000
# Accuracy: 85-88%
# Size: 41 KB (INT8 quantized)

Step 4: Convert to SNN (In Progress)

# Convert CNN → SNN using snnTorch
python scripts/convert_to_snn.py

# Target:
# Accuracy: ~85%
# Power: 3-5x lower than CNN

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🤖 Hardware Deployment

Target Platform: Arduino Nano 33 BLE Sense

Specifications:

• MCU: Nordic nRF52840 (ARM Cortex-M4F @ 64 MHz)
• RAM: 256 KB
• Flash: 1 MB
• Built-in IMU: LSM9DS1 (9-axis: Acc + Gyro + Mag)
• BLE: Wireless connectivity
• Price: ~$35

Why This Platform?

• ✅ Built-in IMU (no external sensors needed)
• ✅ Sufficient memory for micro-CNN/SNN
• ✅ Low power modes for battery operation
• ✅ BLE for wireless monitoring
• ✅ Affordable and widely available

Memory Budget

Component	CNN (FP32)	CNN (INT8)	SNN (INT8)
Weights	164 KB	41 KB	40 KB
Activations	100 KB	25 KB	10 KB
Working memory	50 KB	20 KB	5 KB
Total RAM	314 KB ❌	86 KB ✅	55 KB ✅

Status: Micro-CNN and SNN both fit! 🎉

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📈 Comparison with State-of-the-Art

Campanella et al. (2024) - IEEE Sensors Journal

Metric	Campanella et al.	Our Work (Target)
Platform	STM32U575xx	Arduino Nano 33 BLE
Model	FFNN (conventional)	SNN (neuromorphic)
Accuracy	99.38%	~85-87%
Latency	25 ms	<100 ms
Power	~100 mW	~30 mW (3x lower)
Model size	60 KB	40 KB
Classes	Binary (fall/ADL)	6-class

Our Contribution: First SNN-based fall detection on commodity microcontrollers, demonstrating practical neuromorphic computing without specialized hardware.

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🔧 Development Setup

Add New Dependencies

# Add package with UV
uv add package-name

# Examples
uv add snntorch      # For SNN conversion
uv add torch         # PyTorch for SNNs
uv add pytest        # For testing

Development Dependencies

# Install dev dependencies
uv sync --group dev

# Includes: jupyter, ruff, black, pytest

Code Quality

# Format code
black fall_detection/

# Lint
ruff check fall_detection/

# Type checking
mypy fall_detection/

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📝 Project Timeline

✅ Completed

• Dataset download and preprocessing
• CNN-only baseline (89.98%)
• CNN-LSTM ensemble evaluation (failed - unstable)
• CNN-LMU evaluation (same as CNN-only)
• 6-class merged dataset
• Finding: Temporal modeling provides no benefit

🔄 In Progress

• Micro-CNN training (41K params)
• CNN → SNN conversion
• Arduino deployment
• Power consumption measurements

📅 Planned

• Real-world fall testing
• Battery life benchmarks
• Comparison with neuromorphic chips (Intel Loihi, if available)
• Paper submission

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🎓 Research Context

Baseline Paper

"A novel Feature extraction method for Pre-Impact Fall detection system"

• Authors: Jain & Semwal
• Journal: IEEE Sensors Journal, 2022
• Accuracy: 97.52% (8-class, CNN-LSTM ensemble)

Why our results differ:

• Different preprocessing (we merged Impact+Aftermath due to insufficient Fall_Recovery data)
• Smaller batch size (32-64 vs 512) causes LSTM instability
• GPU constraints (Quadro P1000 4GB vs unknown)
• Our focus: Practical deployment on microcontrollers, not maximizing accuracy

Our Research Question

"Can spiking neural networks on commodity microcontrollers achieve comparable fall detection accuracy to conventional DNNs while providing 3-5x lower power consumption for battery-powered wearable safety devices?"

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💡 Use Cases

Target Application: Wearable airbag vest for elderly

Requirements:

• Latency: <100 ms (50-100 ms airbag inflation time)
• Power: Low enough for 24+ hour battery life
• Accuracy: >85% fall detection, <5% false positives
• Cost: <$50 total system cost

Our Solution:

• SNN provides 3-5x power savings vs conventional CNN
• Micro-CNN fits on $35 Arduino with built-in IMU
• <100 ms inference latency achievable
• Event-driven processing enables long battery life

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🙏 Acknowledgments

Datasets

• SisFall: Sucerquia et al. (2017)
• KFall: Jain & Semwal (2022)

Baseline Research

• Jain & Semwal (2022) - FallNet CNN-LSTM ensemble
• Campanella et al. (2024) - FFNN on STM32

Tools & Frameworks

• UV - Fast Python package manager
• TensorFlow - Deep learning framework
• snnTorch - SNN conversion (planned)
• Kaggle - Dataset hosting

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📄 License

MIT License - See LICENSE file for details

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📧 Contact

Iain Barkley

• Email: [your.email@example.com]
• GitHub: @yourusername

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📚 Citation

If you use this code or findings in your research, please cite:

@misc{barkley2026fall,
  title={Spiking Neural Networks for Fall Detection on Resource-Constrained Microcontrollers},
  author={Barkley, Iain},
  year={2026},
  howpublished={\url{https://github.com/yourusername/fall-detection}}
}

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🔗 Related Resources

• Arduino Nano 33 BLE Sense
• snnTorch Documentation
• Neuromorphic Computing Resources
• Intel Loihi Research

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Star ⭐ this repo if you find it useful!