This project presents the development of a gait recognition system using Tiny Machine Learning (Tiny ML) and Inertial Measurement Unit (IMU) sensors. The system leverages the XIAO-nRF52840 Sense microcontroller and the LSM6DS3 IMU sensor to capture motion data, including acceleration and angular velocity, from four distinct activities: walking, stationary, going upstairs, and going downstairs. The data collected is processed through Edge Impulse, an edge AI platform, which enables the training of machine learning models that can be deployed directly onto the microcontroller for real-time activity classification.The data preprocessing step involves extracting relevant features from the raw sensor data using techniques such as sliding windows and data normalization, followed by training a Deep Neural Network (DNN) classifier for activity recognition. The model achieves over 80% accuracy on a test dataset, demonstrating its ability to classify the four activities effectively. Additionally, the platform enables anomaly detection, further enhancing the robustness of the system. The integration of Tiny ML ensures low-power operation, making it suitable for battery-powered or energy-harvesting devices.

In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task and compared it to other state-of-the-art methods. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.

There are many emerging trends in the tech world, and Machine Learning is one of them. Machine Learning is a subset of Artificial Intelligence where a computer learns from data and analyses its patterns to predict an outcome. Usually, Machine Learning models are trained on big chunks of data to analyze the patterns where these complex models require hours or even days to get processed in the cloud centers. The resultant file of these models also contains a good amount of data. As we all know, data is constantly flowing.

Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI). This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML. TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers.

Most Machine Learning models are created to realize that you want to see 50% Memes and 50% cute cats. To do just that they use huge clusters of computers using CPUs and GPUs and even TPUs to deliver these outstanding state-of-the-art Artificial Intelligence recommendation technologies to you. As we all know this and much more computational hardware is used when training, for example, GPT-3 which costs alone in electricity millions of dollars to train. But most of the time, running inference that means predicting on these models is computationally expensive too. Making these types of energy costly operations happen mostly in data centers far away from your phone.

No-Code Machine Learning: No-code ML is a way of programming ML applications without having to go through the long and tedious procedures of processing, modeling, designing algorithms, deployment, retraining, and more. It is much quicker and simpler to implement and is also cost-effective. Tiny ML: Microcontrollers are the keywords when it comes to Tiny ML. They can shrink deep learning networks to fit into any small hardware system. It is very useful when it comes to cars, refrigerators, and other utility meters. These newly embedded machine frameworks are also enabling high-powered AI-IoT devices to perform efficiently.