Welcome to my personal website!

This is my personal platform to share my story as I progress through my professional career, achieving my professional and personal goals while contributing to my philanthropic interests.

I am a Firmware Test Engineer (QA Lead: Edge Infrastructure) at Catapult Sports, Australia developing world class solutions to support elite sports performance while shaping it’s future.

I specialize in Embedded System Development including CAD, PCB, Embedded Intelligence and IoT system design, Embedded and Multi-platform Software development (Firmware and Applications), Data Analytics, Machine Learning and AI, and End-To-End System design for Remote Hosted, Edge, or Cloud-based Processing or Actuation. Former Research Engineer of Deakin University, leading the Electronic System Development of BioKin Devices to support in clinical diagnosis of Ataxia.

Lahiru Abeysekara


Work Experience

Honours & Awards 

Issued by School of Engineering in 2019 for the final year undergraduate project

Issued by Networked Sensing & Control Research Lab at Deakin University in 2019. Funded by NHMRC grants GNT1101304, APP1129595 and Friedreich’s Ataxia Research Alliance (FARA) ride Ataxia Europe Research Award 

9News Australia, Australian and global newspapers and E-news providers

Issued by Networked Sensing & Biomedical Engineering Research Lab at Deakin University in 2021 for the Biomedical Translational Project in Developing Assistive Devices to Diagnose Ataxia. Funded by the Biomedical Translation Bridge Program delivered by MedTech & Pharma Growth Centre - Medical Research Future Fund, Department of Health, Federal Government of Australia.

Research Publications

Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test. Inertial Measurement Unit (IMU) data were collected from a sensor attached to their chest of 53 subjects while they performed the test. The corresponding clinical scores were also tabulated. Using this data, 99 features were extracted to quantify acceleration, tremor and displacement of body sway. These features were filtered to identify the subset that better characterize the distinctive behavior of CA subjects. Elastic Net Regression model resulted a greater agreement (0.70 Pearson coefficient) with the clinical SARA scores. The overall results indicated that data from a single IMU sensor is sufficient to accurately assess balance in CA. The significance of this study is that evaluation of balance using Recurrence Quantification Analysis produces a comprehensive framework for the assessment of CA. 

Read more in IEEE Xplore

Cerebellar Ataxia is a neurological disorder without an approved treatment. Patients will have impaired and uncoordinated motor functionality making them unable to complete their day-to-day activities. Ataxia clinics are established around the world to facilitate research and rehabilitate patients. However, the patients are generally evaluated by human - observation. Therefore, machine learning based data analysis is popular on motion captured via sensors. There are many neurological tests designed to analyse the motor impairments in different domains (such as upper limb, lower limb, gait, balance and speech). Clinicians follow scoring protocols to record the severity of patients for each domain test. This paper delivers a clinical assessment platform combining 12 neurological tests in 5 domains. It captures motion (from BioKin sensors), haptic and audio data (from the tablet or laptop screen). A data analysis system is hosted in a remote server which evaluates data to produce a severity score via different models built for each neurological test. The assessment platform clients and server communicate via a cloud buffer system. The scores input by the clinicians and predicted by the machine learning models are logged in the cloud database. This enables clinicians and doctors to view and compare the history of patient diagnosis. The server system is structured for automated score model upgrades via prompted approval. Thus, the most viable scoring model could be accommodated for each test based on longitudinal studies. 

Read more in IEEE Xplore

Neuromorphic chips are electronic hardware mimicking neurons in human brain in an electronic structure. These ASICs (Application Specific Integrated Circuits) provide artificial neural networks with computational power comparatively higher than most neural networks generated by software algorithms. 'CM1K' is an electronic chip in this family of products. It has a parallel neural network of 1024 neurons. These neurons provide K-Nearest Neighbor (KNN) data classification. The chip requires to be embedded in an electronic system to access all its capabilities. This paper deliver a novel hardware system embedding CM1K neuromorphic chip. The system was implemented in image and video frame analysis for evaluation. The results prove that the system could benefit various applications including security, asset management, home appliances, mail sorting and manufacturing. Since the embedded system provide opportunity to integrate AI in to simple electronics, it helps on extending AI applications. 

Read more in IEEE Xplore



**Funded by National Health and Medical Research Council, Australia 

**Funded by Friedreich’s Ataxia Research Alliance (FARA) rideATAXIA Europe research award 

Project: BioKin IoT and cloud computing system development 

Project: BioKin IoT and cloud computing system development 

**Funded by Medical Research Future Fund

A smart bag integrating IoT, RFID technology and cloud processing. The project was published and broadcasted in Australian and global media.

Developed an electronic platform integrating CM1K Neuromorphic chip and ATMEGA 2560V microcontroller 

Lbrain (electronic AI hardware) platform development for universal applications. Cognitive Computer based on Lbrain. AI integrated multi - purpose software development (UWP - C#). IoT sensors for AI integration (Windows 10 IoT Core).  

Integrating AI ASICs for embedded system development.

Planning and designing a safe power distribution substation to suit an estimated daily load profile of customers. 

The system was designed to provide power to an estimated load and area.

An animatronic head was designed and built to demonstrate human facial expressions.

Research project presenting control strategies used in induction motor drives.

The system was designed in an academic group project. It was proposed for village Sandikola in Nepal as a solution for their water system. Supported by Engineers Without Borders (EWB) Australia.


A microcontroller based High Voltage - 7 segment display system to display real time machine performances from machine pulse counts, contacts and timers. The solution was integrated in a corrugated carton manufacturing plant.