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2026 Arduino TinyML Projects · Gesture · Keyword Spotting · Vision · Anomaly Detection

Arduino TinyML Projects

Best final-year topics on machine learning on microcontrollers — TensorFlow Lite for Microcontrollers, Edge Impulse, gesture recognition, keyword spotting, tiny vision and anomaly detection on Arduino Nano 33 BLE Sense, ESP32 and similar boards for BE, BTech and MTech students.

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50+
TinyML Topics
6
Core Domains
2026
Edge ML Ready
Gesture / Motion Audio / KWS Vision Anomaly Detection Sensors / Environment Tools · Deploy

Arduino TinyML Projects — On-Device Machine Learning for Microcontrollers

TinyML runs trained neural networks on resource-constrained boards such as the Arduino Nano 33 BLE Sense and ESP32. Final-year projects that collect sensor data, train compact models (often with Edge Impulse or TensorFlow), quantize them, and deploy with TensorFlow Lite for Microcontrollers demonstrate real edge AI skills valued by industry.

Below are 50+ topics across gesture/motion, audio keyword spotting, tiny vision, anomaly detection, environmental sensing, deployment tools and applied systems, with the boards and software typically used.

TFLite Micro Edge Impulse Nano 33 BLE Sense ESP32 / S3 CMSIS-NN Arduino IDE / PIO
# Arduino TinyML Project Topic Tools Used
✋ Gesture & Motion Recognition (IMU)
01GestureHand Gesture Recognition with Onboard IMU (Wave, Punch, Circle)Nano 33 BLE Sense, Edge Impulse / TFLite Micro
02GestureActivity Classification: Walk, Run, Idle, Stairs from AccelerometerIMU, TFLite Micro, Arduino
03GestureFall Detection System for Wearable DemoAccelerometer, Edge Impulse, BLE alert
04GestureMagic Wand Style Gesture Control of LEDs or RelayNano 33, TFLite Micro, digital out
05GestureRepetition Counter for Exercise (Squats, Push-ups) via IMUIMU, threshold + ML hybrid
06GestureOrientation / Tilt State Classifier for Smart MountAccelerometer + gyro, TFLite
07GestureMulti-Class Gesture Set with Confusion Matrix EvaluationEdge Impulse, Arduino deploy
08GestureContinuous Gesture Streaming with Sliding Window FeaturesTFLite Micro, ring buffer
🎙️ Audio · Keyword Spotting · Sound Classification
09AudioKeyword Spotting (“Yes/No” or Custom Wake Words) on MCUPDM mic, TFLite Micro, Edge Impulse
10AudioEnvironmental Sound Classification (Glass Break, Alarm, Dog Bark)Microphone, MFCC + TinyML model
11AudioVoice Command Interface for Simple Home AutomationKWS model, relay/LED control
12AudioCough / Sneeze Detection Prototype for Health Monitoring DemoMic, Edge Impulse, Nano 33
13AudioAudio Event Detection with Duty-Cycled Low-Power ListeningPDM mic, sleep modes, TFLite
14AudioComparison of Spectrogram vs Raw Waveform Models for KWSEdge Impulse, Arduino
15AudioMulti-Keyword Spotting with Rejection of Unknown WordsTFLite Micro, silence/unknown class
📷 Tiny Vision · Image Classification · Detection
16VisionPerson / No-Person Detection with Low-Resolution CameraOV7670 / Himax, TFLite Micro, ESP32-CAM
17VisionSimple Object Classification (e.g. Fruit or Tool Classes) On-DeviceCamera module, Edge Impulse FOMO / classification
18VisionFOMO-Style Object Detection for Counting Small ObjectsEdge Impulse FOMO, Arduino / ESP32
19VisionGesture Recognition from Camera (Hand Pose Classes)Camera, TinyML vision model
20VisionOccupancy Detection for Room / Desk Using VisionCamera, TFLite, occupancy logic
21VisionColor / Shape Sorting Concept with Tiny Vision ClassifierCamera, classification model
22VisionModel Size vs Accuracy Trade-off for On-Device Image ModelsQuantization, TFLite Micro benchmarks
⚠️ Anomaly Detection & Predictive Maintenance
23AnomalyVibration Anomaly Detection for Motor / Fan HealthAccelerometer, Edge Impulse anomaly, Arduino
24AnomalyCurrent / Power Signature Anomaly for Appliance MonitoringCurrent sensor, TinyML, ESP32
25AnomalyAcoustic Anomaly Detection (Unusual Machine Sounds)Mic, autoencoder / anomaly block
26AnomalyTemperature Spike / Drift Anomaly for Equipment SafetyTemp sensor, threshold + ML
27AnomalyComparative Study: Threshold Rules vs Learned Anomaly ModelEdge Impulse, Arduino logging
28AnomalyUnsupervised Feature Learning for Sensor Anomaly on MCUEdge Impulse, TFLite Micro
🌡️ Environmental & Multi-Sensor Classification
29EnvIndoor Air Quality Event Classification (VOC / Humidity Patterns)Gas / humidity sensors, TinyML
30EnvWeather / Micro-Climate State Classifier from Multi-Sensor BoardTemp, humidity, pressure, ML
31EnvSoil Moisture + Environment Based Irrigation Decision ModelSoil sensor, Arduino, simple model
32EnvLight / Motion Combined Occupancy InferencePIR + light, classification
33EnvMulti-Sensor Fusion Model (IMU + Audio or IMU + Env)Nano 33 sensors, Edge Impulse
34EnvLow-Power Sensor Sampling Strategy for Continuous TinyMLSleep modes, duty cycle, TFLite
🛠️ Tools · Quantization · Deployment · Evaluation
35ToolsEnd-to-End Edge Impulse Pipeline: Collect → Train → Deploy to ArduinoEdge Impulse, Arduino library
36ToolsTensorFlow Lite Micro Deployment from Keras / TF ModelTFLite Converter, Arduino_TensorFlowLite
37ToolsINT8 Quantization Impact on Accuracy and Latency on MCUTFLite Micro, benchmarking sketch
38ToolsMemory and Flash Footprint Analysis of TinyML ModelsArduino IDE size report, model stats
39ToolsCMSIS-NN Optimised Kernels vs Generic TFLite MicroCMSIS-NN, ARM boards
40ToolsOver-the-Air Model Update Concept for ESP32 TinyML DeviceESP32, OTA, model storage
41ToolsOn-Device Inference Latency Profiling and Optimisationmicros() timing, TFLite Micro
42ToolsData Collection App / Serial Logger for High-Quality Training SetsArduino Serial, Python logger
🏥 Applied Systems & Product-Style Demos
43AppliedSmart Doorbell / Knock Classification with Audio TinyMLMic, KWS/sound model, notification
44AppliedWearable Activity Tracker Prototype with On-Device ClassificationNano 33, BLE to phone
45AppliedIndustrial Machine Health Monitor (Vibration Anomaly + Alert)IMU, anomaly model, ESP32
46AppliedAccessible Switch: Gesture or Voice to Control ApplianceGesture/KWS, relay
47AppliedSmart Agriculture Node: Soil + Environment Decision ModelSensors, TinyML, low power
48AppliedClassroom / Lab Equipment Usage Logger with Vision or IMUCamera or IMU, classification
🔬 Research-Oriented & Comparative Topics
49ResearchBoard Comparison: Nano 33 BLE Sense vs ESP32-S3 for Same ModelBoth boards, same TFLite model
50ResearchEffect of Window Size and Stride on Gesture AccuracyEdge Impulse, systematic experiments
51ResearchFew-Shot / Transfer Learning for New Anomaly Classes on MCUEdge Impulse, limited data
52ResearchInput Resolution vs Accuracy/Latency for Tiny Vision ModelsCamera, multiple resolutions
53ResearchReproducible TinyML Experiment Protocol for Student ProjectsConfigs, logs, metrics templates
54ResearchPersonalisation: User-Specific Gesture Models with Small DataEdge Impulse, transfer learning
55ResearchNoise Robustness of On-Device Keyword SpottingAugmented audio, TFLite Micro

Topics use widely available boards (Arduino Nano 33 BLE Sense, ESP32) and open tools (Edge Impulse, TensorFlow Lite Micro). Contact us for reference material, sketches, model notes, evaluation setup, university-format report, PPT and viva Q&A for any topic above.

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Why Choose Us for Arduino TinyML Projects?

Bangalore-based guidance for BE, BTech and MTech students building on-device ML with Arduino and ESP32.

Gesture / IMU

Hand gestures, activity recognition and fall detection with onboard IMU and clear evaluation.

Keyword Spotting

Wake-word and environmental sound models running fully on-device with TFLite Micro.

Tiny Vision

Person detection, FOMO object counting and classification with low-resolution cameras.

Anomaly & Deploy

Vibration and sensor anomaly detection plus full Edge Impulse / TFLite Micro deployment pipelines.

Frequently Asked Questions — Arduino TinyML Projects

Top topics include IMU gesture recognition, keyword spotting, person/object detection with tiny cameras, vibration anomaly detection, multi-sensor environmental classifiers, and full Edge Impulse or TFLite Micro deploy pipelines on Nano 33 BLE Sense or ESP32.
Arduino Nano 33 BLE Sense, Seeed XIAO, ESP32/ESP32-S3, Arduino IDE or PlatformIO, TensorFlow Lite for Microcontrollers, Edge Impulse, CMSIS-NN, and the Arduino_TensorFlowLite library.
Yes. Packages include reference material, model training notes, Arduino sketches, deployment steps, evaluation metrics, university-format report, PPT and viva Q&A.
TinyML is machine learning on resource-constrained devices such as microcontrollers. Models are trained (often on PC or cloud), then quantized and deployed with runtimes like TensorFlow Lite for Microcontrollers so inference runs locally with low power and low latency.