Applications of TinyML making specialised education more accessible

This is the 3rd and final article in a 3 part series featuring the applications of tinyML. The series introduces the concept of tinyML and explains some selected applications of the technology in various fields like sustainability, healthcare, and education.

Education is the first step towards innovation. It is an integral part of personal development and equips the learners with the tools necessary to go ahead and solve the challenges faced by humanity. While tinyML is a great tool to develop pedagogical interventions for improving the quality of education in different streams, it is in itself a vast subject that every science and technology student must be familiar with.

In our previous article about the applications of tinyML, we explained how it aids medical research and improves healthcare. This article continues the series by showcasing two more applications of tinyML for education.

Using tinyML for Neuroscience in K12 education: tinyML4STEM

Anything and everything concerned with the central nervous system is a part of neuroscience. It is a multidisciplinary field that involves a scientific study of how the brain coordinates with the body and makes various body functions possible. The study is not just limited to humans but can also be extended to animals and even plants.

Roughly 20% of the world’s population has suffered from a neurological disorder at some point in their lives, and none of the disorders have a cure yet. The only way to make new discoveries in the domain is by promoting research and education.

It is also interesting to note that the most fundamental unit of Machine Learning (ML) is called a neuron and the whole concept and modelling of Artificial Intelligence (AI) based systems came from neuroscience. This makes it essential to talk about the specialised subject with the students of computer science to make them understand the underlying principles of the technology.

But the problem with neuroscience education has always been the affordability and availability of experiment kits. Unlike many other Science Technology Engineering Management (STEM) fields, the apparatus for performing neuroscience experiments can not be acquired easily by schools and colleges. Graduate students opting for a specialisation in the area are the only ones to get their hands on these experiment kits.

Greg Gage and Tim Marzullo realised this problem back in 2008 when they were pursuing PhD at the University of Michigan. The duo tried explaining neuroscience concepts to school children but couldn’t do so without showcasing practicals. There was no way to bring expensive graduate college equipment to schools. To solve this issue and make neuro-science kits affordable and readily available, Greg and Tim founded Backyard Brains.

Illustrations like these can help kids grasp even complex concepts easily. Source: Backyard Brains

The organisation today makes low-cost neuroscience trainers, DIY kits, tools, software, conducts workshops and a lot more to democratise neuroscience education.

Neuroscience trainer kits by Backyard Brains are great alternatives for schools to introduce the students to the field. Source: Backyard Brains

Neuroscience signals like Electromyogram (EMG), Electrooculogram (EOG), Electrocardiogram (EKG), etc., can be used to get various cues about what is going on inside the body.

When neurons fire electrical impulses from the motor cortex, the signals flow through the spinal cord to the parts of the body involved in the action. By putting small electrodes on the skin near the areas where the signals can be picked up, it is possible to observe them via an EMG. The impulses being electrical in nature can be amplified as a single-dimensional signal and visualised easily. These single-dimensional signals are beautiful for ML and are also quite easy to be analysed.

A snapshot from tinyML talks where Greg demonstrates how EMG signals can be visualised with a low-cost training kit. Source: tinyML YouTube

The patterns in these signals might not be as apparent to humans as they are to the tinyML algorithms, which can classify and map them with different human behaviours. An implementation of such a technique can be used to develop a brain-machine interface that can help make neuroprosthetics. 

EOG is another example of a neuro signal that measures the corneo-retinal standing potential of the eye. In other words, it helps understand eye movements. A trained TinyML system can be used to accurately detect eye blinking and movements by feeding the signal to the input.

The spikes that appear on the EOG are due to eye blinking. Source: tinyML YouTube

Then there’s EKG generated by the electrical impulses from the heart. For different people, the patterns of EKG are observed to be similar when looking at the same object. This can be used to predict if the person is telling the truth. In medical applications, signals can be used to check the functioning of the brain. A sample tone is played to the patient, and electric potential signals are observed. When the tone being played changes, the subconscious mind should ideally change the way it reacts, which should be evident in the electric potential signals.

‘Hackerhand’, a robotic arm mimicking hand movements based on EKG signals. Source: tinyML YouTube

It is also interesting to note that the signals are generated slightly before someone is about to take action, making it possible for tinyML systems to be able to predict the behaviour in advance. A similar fun project has been developed at Backyard Brains.

Readers can participate in many more such projects or share ideas by visiting Backyard Brains.

TinyML Open Education Initiative: tinyMLedu

Over the past two decades, ML has been through a journey from a purely academic and theoretical discipline to a widely spread technology in use. It won’t be an understatement to say that technology has touched almost every industry and is now ubiquitous technology. It is expected that the interest in the field will only grow from here, at least in the near foreseeable future. Given its amazing potential in solving some of the greatest challenges faced by humanity, it is important that more people are introduced to technology in a systematic way.

Instructors of the tinyML specialisation

Unlike traditional ML, it is possible to run tinyML algorithms on low-cost and low-power embedded systems, which solves a major problem related to the unavailability of powerful hardware for learners. But the shortage of ML educators is still something that needs to be addressed.

A joint initiative by Harvard and Google, developed as an academic and industry collaboration, aims to address the challenges by introducing tinyML to the masses with a Massive Open Online Course (MOOC) on edX. 

The courses that are a part of the tinyML specialisation go beyond teaching the students about the subject by diving deeper into the practical applications and industrial product development lifecycle. The next generation of students needs to understand the end-to-end full-stack development of tinyML projects to be able to gain a complete understanding of the subject.

A diagram providing an overview of topics explored in tinyML specialisation. Source: [3]

The tinyML specialisation, jointly developed by Harvard and Google, is a set of self-paced learning modules that can be taken from anywhere. The specialisation includes four courses:

1. Introduction to tinyML: The first course covers the basics of ML and embedded systems. Everything from the concept to gathering data and training the algorithms is explained.
2. Applications of tinyML: The second course builds on the foundation by explaining some parts of the code behind the most widely used tinyML applications. It also introduces the learners to the principles of keyword spotting, visual wake words, anomaly detection, and dataset engineering.
3. Deploying tinyML: The third course teaches how code is written, optimised, and deployed for embedded systems. This is where microcontrollers are introduced, and students can purchase a kit for a nominal price (US$49.99) to get hands-on experience.
4. Scaling tinyML [Optional]: Once the learners are familiar with the design, development and deployment of tinyML, the final course covers the more advanced aspects related to producing tinyML solutions for the masses.

Each course includes several activities such as collabs, hands-on labs, quizzes, readings, assignments, tests, and discussion-forum participation.

The key challenges for students taking up the tinyML specialisation can be limited hardware accessibility and the requirement of being familiar with programming. To address the challenges associated with hardware accessibility, open-source emulation platforms like renode.io from Antmicro are being tested to allow learners to try out their code without the hardware. The other challenge can be solved by platforms like Edge Impulse, which are lowering the barriers to enable people with little to no programming knowledge to implement tinyML solutions.

Anyone with interest in tinyML can visit tinyML Professional Certificate | edX and enrol for the course for free. The course material is available on GitHub.

Conclusion

Though the project kits or MOOCs are not a complete solution to train professional neuroscience researchers or tinyML solution architects, they offer a great place for beginners to start. The kits and courses are designed in such a way that the students can engage in fun activities while they experiment and learn new stuff. This makes the experience memorable and develops an interest in the subject, which could lead more people to choose the specialised fields and make new discoveries. 

This article concludes our tinyML applications series.

About tinyML Foundation

With its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.

The introductory article familiarised the readers with the concept of tinyML.

The first article explained some possibilities of tinyML applications in sustainable technologies.

The second article is about tinyML applications aiding healthcare and medical research.

The third and final article shall cover the tinyML applications in pedagogy and education.

References

[1] Marzullo TC, Gage GJ (2012) The SpikerBox: A Low Cost, Open-Source BioAmplifier for Increasing Public Participation in Neuroscience Inquiry. PLOS ONE 7(3): e30837. https://doi.org/10.1371/journal.pone.0030837.

[2] Gregory J. Gage, The Case for Neuroscience Research in the Classroom, Neuron, Volume 102, Issue 5, 2019, Pages 914-917, ISSN 0896-6273, https://doi.org/10.1016/j.neuron.2019.04.007.

[3] Vijay J. Reddi et. al., Widening Access to Applied Machine Learning with TinyML, arXiv:2106.04008v2 [cs.LG], Jun 2021, [Online], Available from: https://arxiv.org/abs/2106.04008.