Embedded Systems Jobs for Freshers: Skills & Career Guide

What Is TinyML?

TinyML stands for Tiny Machine Learning. It refers to running machine learning inference on small, low-power devices such as microcontrollers.

Traditional machine learning applications may use powerful CPUs, GPUs, cloud servers, or edge computers. TinyML works under much tighter hardware constraints.

A typical TinyML device may have:

• Limited RAM
• Limited flash storage
• A low-power processor
• Battery-powered operation
• Sensor inputs
• No continuous internet connection
• Limited computational resources

Despite these limitations, the device can still execute a compact machine learning model.

The important distinction is that TinyML generally focuses on on-device machine learning. The model is trained using more powerful computing resources, but the resulting model is optimized and deployed onto the embedded device for inference.

For example:

This architecture is particularly useful when a device needs to make decisions quickly and locally.

What Is Gesture Recognition?

Gesture recognition is the process of identifying a particular movement or physical action from sensor data.

A gesture can be detected using several types of input, including:

• Accelerometers
• Gyroscopes
• IMUs
• Cameras
• Touch sensors
• Proximity sensors
• Pressure sensors

For embedded systems, motion sensors are especially useful because they can capture movement without requiring a camera.

Consider a wearable device containing an accelerometer.

When the user moves the device, the sensor produces acceleration values along multiple axes:

• X-axis → Horizontal movement
• Y-axis → Vertical movement
• Z-axis → Depth movement

The resulting sequence of values creates a motion pattern.

A machine learning model can learn that different patterns correspond to different gestures.

For example:

• Shake → Class 1
• Swipe Left → Class 2
• Swipe Right → Class 3
• Tap → Class 4
• Circular Motion → Class 5

The microcontroller can then classify new sensor data and determine which gesture was performed.

How TinyML for Gesture Recognition Works

A complete TinyML gesture recognition system usually contains several stages.

The overall pipeline can be represented as:

Each stage has an important role.

1. Collecting Sensor Data

The first step is collecting movement data.

An accelerometer or IMU continuously measures changes in movement. The device records these measurements at a particular sampling frequency.

For example, suppose an accelerometer is configured to collect data at 50 samples per second.

For every sample, the system may record:

• X acceleration
• Y acceleration
• Z acceleration

A short movement therefore produces a sequence of measurements rather than a single value.

For example:

When the user performs a particular gesture, the sequence forms a characteristic pattern.

The more representative the collected dataset is, the better the model can learn the differences between gestures.

2. Creating a Labeled Dataset

Machine learning requires examples.

Suppose we want to recognize three gestures: Left, Right, and Shake.

The collected sensor data needs to be associated with the correct label.

A dataset may therefore contain pairs such as:

This process is called data labeling.

For a reliable model, the dataset should contain sufficient variation.

For example, users may perform the same gesture:

• Slowly
• Quickly
• With different amounts of force
• At different angles
• With different device orientations

If the training data contains only one very specific movement style, the model may perform poorly when exposed to different users or real-world conditions.

This is one of the most important considerations in sensor-based machine learning.

3. Sensor Data Preprocessing

Raw sensor data is not always ready to be passed directly into a machine learning model.

Preprocessing can help make the data more consistent.

Common preprocessing operations include:

Normalization — sensor values can be scaled into a suitable range.

Filtering — filters can be applied to reduce unwanted high-frequency noise.

Segmentation — continuous sensor data can be divided into smaller windows.

For example:

Each window can then be classified independently.

This is important for real-time gesture recognition, because the device does not need to wait for an entire long recording before making a prediction.

4. Feature Extraction

Feature extraction involves identifying useful information from sensor data.

Traditional machine learning systems may calculate features such as:

• Mean
• Minimum
• Maximum
• Standard deviation
• Variance
• Signal energy
• Peak values
• Frequency components

For example, a strong shake may produce a substantially different acceleration pattern compared with a slow swipe.

Modern neural networks can also learn useful representations directly from sensor sequences.

The choice between manually engineered features and learned features depends on the model architecture, available hardware, dataset, and application requirements.

5. Training the Machine Learning Model

After preparing the dataset, the model can be trained.

Different algorithms can be considered for gesture classification, including:

• Decision Trees
• Random Forest
• Support Vector Machines
• K-Nearest Neighbors
• Neural Networks
• 1D Convolutional Neural Networks

For TinyML applications, the model should not be selected only based on accuracy. The model must also fit the target embedded hardware.

Developers need to consider:

• Model size
• RAM requirements
• Flash usage
• Processing time
• Power consumption
• Accuracy
• Number of operations

A highly complex model may provide good accuracy on a computer but may not be practical for a small microcontroller.

The goal is therefore to develop a model that provides an appropriate balance between accuracy and resource consumption.

TinyML Model Optimization

Model optimization is one of the most important steps when moving machine learning from a computer to a microcontroller.

Microcontrollers have considerably fewer resources than desktop computers or cloud servers.

Optimization techniques can include:

• Quantization
• Reducing model layers
• Reducing the number of parameters
• Removing unnecessary operations
• Selecting an appropriate input size
• Using efficient model architectures

Quantization

Quantization can reduce the numerical precision used by a model.

For example, instead of storing model values using higher-precision floating-point representations, a model may be converted to a lower-precision integer representation where appropriate.

This can reduce:

• Model size
• Memory requirements
• Computational requirements

However, optimization should always be validated because aggressive optimization can affect model accuracy.

Deploying TinyML on a Microcontroller

Once the model has been trained and optimized, it needs to be converted into a format suitable for embedded deployment.

Frameworks such as TensorFlow Lite for Microcontrollers are designed for deploying machine learning models on microcontroller-class hardware.

A simplified deployment process looks like this:

The embedded firmware handles both the sensor and machine learning pipeline.

For example:

If the model identifies a “Swipe Right” gesture, the firmware can execute a corresponding action.

ESP32 Gesture Recognition Using TinyML

ESP32 gesture recognition is a popular area for TinyML experimentation because ESP32-based development boards provide wireless connectivity, GPIO interfaces, and sufficient processing resources for many small machine learning applications.

A basic prototype can combine:

• ESP32
• Accelerometer or IMU
• TinyML model
• LED or display
• Embedded C/C++ firmware

For example:

The ESP32 can then perform an action based on the recognized class.

For students, this type of project provides practical exposure to both embedded programming and machine learning.

STM32 and TinyML

STM32 microcontrollers are also suitable for many TinyML embedded systems applications.

STM32 devices are widely used in embedded development and provide a broad range of microcontrollers with different levels of processing capability, memory, peripherals, and power characteristics.

A gesture recognition project using STM32 can combine:

• STM32 microcontroller
• Accelerometer or IMU
• C/C++ firmware
• Sensor drivers
• Signal processing
• Machine learning inference
• UART/SPI/I2C communication

The sensor may communicate with the microcontroller through interfaces such as I2C or SPI.

The STM32 then processes the sensor stream and passes the required input to the embedded ML model.

TinyML Gesture Recognition with an IMU

An IMU can provide more information than a basic accelerometer.

Depending on the sensor, an IMU may combine:

• Accelerometer
• Gyroscope
• Magnetometer

This allows the system to capture both linear acceleration and rotational movement.

For example, two gestures might produce similar acceleration patterns but different rotational patterns.

Using multiple sensor signals can potentially make the classification problem easier, although it also increases the amount of data that needs to be processed.

This is known as sensor fusion when information from multiple sensors or sensor axes is combined.

For wearable and motion-based applications, sensor fusion can be an important part of the design.

Edge Impulse for TinyML Gesture Recognition

Tools such as Edge Impulse can simplify the process of developing sensor-based machine learning applications.

A typical workflow includes:

• Collect sensor data
• Label the data
• Create training and testing datasets
• Configure signal processing
• Train the machine learning model
• Evaluate model performance
• Optimize the model
• Deploy it to supported embedded hardware

This type of workflow is useful for learning because it exposes developers to the complete TinyML lifecycle.

Instead of treating machine learning and embedded programming as two separate subjects, developers can see how the trained model becomes part of an actual embedded application.

Why Use TinyML Instead of Cloud-Based Machine Learning?

Cloud-based machine learning and TinyML solve different types of problems.

In a cloud architecture:

With TinyML:

The second approach can reduce dependence on network connectivity.

Latency — local inference can avoid the network round trip required by a cloud-based system. This can be useful when the application requires fast responses.

Connectivity — a TinyML device can perform inference even when an internet connection is unavailable, provided the application does not otherwise require network access.

Privacy — raw sensor data can potentially remain on the device. This can be useful for applications where continuous transmission of user movement data is undesirable.

Bandwidth — instead of transmitting every sensor measurement, the device can send only relevant results.

For example:

This can significantly change the communication requirements of the application.

Real-World Applications of TinyML Gesture Recognition

The concept of TinyML applications in embedded systems extends across several industries.

1. Wearable Devices

Wearable devices can use motion sensors to recognize user movements.

Possible applications include:

• Gesture-based navigation
• Device control
• Activity recognition
• Human-machine interaction
• Smart wearable interfaces

A user could perform a predefined gesture instead of pressing a physical button.

2. Smart Home Devices

Gesture recognition can provide another interaction method for smart home systems.

For example:

The actual commands depend on the device and application design.

3. Robotics

A wearable sensor can act as a simple controller for a robot.

For example:

The TinyML model identifies the gesture, while the communication system sends the corresponding command to the robot.

This creates a simple form of human-robot interaction.

4. Industrial Automation

Gesture recognition can be used as an additional human-machine interface in some industrial applications.

A worker could perform predefined movements to trigger specific commands.

However, industrial systems must be designed with appropriate safety mechanisms. Gesture recognition should not be treated as a replacement for safety-critical controls without proper engineering validation.

5. Assistive Technology

Gesture recognition can also be used to develop alternative interfaces.

A device could recognize predefined movements and convert them into digital commands.

This may allow physical movement to become an input method for an electronic system.

Challenges When Building a TinyML Gesture Recognition System

Building a working prototype is relatively straightforward. Building a system that performs reliably in real-world conditions is more challenging.

Dataset Quality

The quality of the training dataset directly affects the machine learning model.

A dataset should represent the conditions under which the device will actually operate.

For example, if a wearable will be used by many people, training data should ideally contain movement variations from multiple users.

Sensor Orientation

The physical orientation of the sensor can change the resulting data.

A gesture performed with the sensor positioned differently may produce different X, Y, and Z values.

Therefore, the model needs to be designed and tested with realistic orientation changes.

False Positives

A gesture recognition model can sometimes classify an unintended movement as a valid gesture.

For example:

This can be particularly problematic when a gesture triggers an important device operation.

Developers can address this through better datasets, confidence thresholds, signal processing, and application-level logic.

Memory Limitations

The model must fit within the memory available on the microcontroller.

The developer needs to consider:

• Model size
• Tensor arena/RAM requirements
• Firmware size
• Sensor buffers
• Communication buffers
• Runtime memory

A model that exceeds available memory cannot simply be deployed without changing the design.

Power Consumption

Battery-powered embedded devices must carefully manage energy.

Power consumption can come from:

• Microcontroller processing
• Sensors
• Wireless communication
• Displays
• Continuous sampling

An optimized TinyML application can potentially reduce unnecessary communication and processing, but the complete system must be measured rather than assuming that ML inference is always the dominant or negligible energy cost.

How to Build a TinyML Gesture Recognition Project

A practical project can be developed through the following stages.

Step 1: Select the Hardware

Choose a microcontroller such as ESP32 or STM32 and an appropriate motion sensor.

Step 2: Define the Gestures

Start with a small number of classes, for example Left, Right, and Shake.

Adding too many gestures at the beginning can make data collection and classification more difficult.

Step 3: Collect Sensor Data

Record multiple examples of each gesture.

Include realistic variations in speed, force, orientation, and users.

Step 4: Label the Data

Every training sample needs the appropriate gesture label.

Step 5: Prepare the Dataset

Split the data into training, validation, and testing sets.

Avoid evaluating the model only on the same data used for training.

Step 6: Train the Model

Experiment with an appropriate classification algorithm.

For sensor-based applications, small neural networks or other lightweight classification models can be considered.

Step 7: Evaluate the Model

Measure more than training accuracy.

Consider:

• Validation accuracy
• Test accuracy
• Confusion between gesture classes
• False positives
• False negatives
• Inference time

Step 8: Optimize the Model

Reduce the computational and memory requirements as needed.

Step 9: Deploy the Model

Convert the optimized model into a format suitable for the target microcontroller and integrate it with the firmware.

Step 10: Test on Real Hardware

This is essential.

A model that performs well on a development computer may behave differently on the actual embedded device.

Measure the complete system under realistic operating conditions.

Example TinyML Gesture Recognition Architecture

A practical embedded AI architecture can look like this:

This architecture demonstrates an important concept in embedded AI: the machine learning model is not the entire application.

The final system combines hardware, firmware, sensor processing, machine learning, and application logic.

TinyML vs Traditional Embedded Programming

Traditional embedded programming often relies on deterministic rules.

For example:

IF acceleration > threshold
THEN trigger action

This works well when the signal is predictable.

However, human gestures can vary significantly.

A machine learning approach can learn patterns from examples:

This does not mean machine learning should replace conventional embedded logic.

In many practical systems, both approaches can work together.

For example:

This hybrid approach can combine signal processing, machine learning, and deterministic application logic.

Skills Required to Work on TinyML Projects

Developing TinyML embedded systems requires knowledge from multiple areas.

Embedded Programming — knowledge of C, C++, microcontrollers, GPIO, timers, interrupts, UART, SPI, and I2C.

Electronics — understanding sensors, voltage levels, digital and analog signals, sensor interfaces, and power management.

Machine Learning — basic knowledge of classification, training and testing, datasets, model evaluation, overfitting, and feature engineering.

Python — useful for data preparation, dataset analysis, model development, visualization, and machine learning experimentation.

Embedded AI — developers should also understand model optimization, quantization, on-device inference, memory constraints, latency, and power consumption.

TinyML sits at the intersection of these technologies.

Future of TinyML and Embedded AI

The development of AI at the edge is changing how intelligent embedded devices are designed.

Instead of relying entirely on cloud infrastructure, devices can increasingly perform useful inference locally.

Future embedded AI systems may combine:

• TinyML
• Low-power microcontrollers
• AI accelerators
• Advanced sensors
• Sensor fusion
• Wireless communication
• Edge computing

This can enable applications where devices continuously observe sensor inputs and respond locally.

Gesture recognition is only one example.

The same TinyML concepts can be applied to:

• Keyword spotting
• Anomaly detection
• Predictive maintenance
• Vibration analysis
• Activity recognition
• Environmental monitoring
• Audio classification
• Machine condition monitoring

The common principle is the same:

Conclusion

Platforms and technologies such as ESP32, STM32, TensorFlow Lite for Microcontrollers, and Edge Impulse provide practical ways to explore these concepts.

For embedded systems students, gesture recognition is particularly useful as a project because it combines C/C++, microcontrollers, sensors, signal processing, machine learning, and embedded AI in one application.

The key to a successful TinyML project is not simply achieving high model accuracy in a development environment. The model must also work reliably on the actual embedded hardware while meeting the application’s requirements for latency, memory, power consumption, and real-world robustness.

As embedded devices become increasingly intelligent, TinyML provides a practical path toward building smart embedded devices that can sense, understand, and respond locally.