Introduction

ETH Zurich is one of the leading universities of the world with a strong focus on science and engineering. In 2010 it established the Singapore-ETH Centre (SEC) in collaboration with the National Research Foundation (NRF) to do interdisciplinary research on pressing problems.

SEC is undertaking a research programme on "Built Environment and physical activity in Falls and Arthritis study (BE-FIT)" in collaboration with the Woodlands Health (WH) and the National Health Group (NHG), the Rehabilitation Research Institute of Singapore (RRIS), the Nanyang Technological University (NTU), and SingHealth. It addresses imminent health challenge on moving away from "sickcare" and pivoting towards preventive healthcare as part of the nationwide effort on Healthier SG. Within BE-FIT we envision motivating vulnerable older adults to engage in healthy behavior by providing recommendations on improving accessibility (as well as perception thereof), in urban environment for uptake of physical activity. A deeper understanding of the interactions and interplay between BE and the high burden of falls and OA as proposed within the BE-FIT is crucial towards informing data-driven decisions on urban design and how the mobility-impaired elderly interact with their physical environment.

Project background

Built Environment and physical activity in Falls and Arthritis (BE-FIT) study is looking for Research Engineering to work on wearables based functional assessment and longitudinal monitoring for fall risk prediction. The aim is to develop automated machine learning (ML) and wearables data processing algorithm pipelines for processing, analyzing, predicting and visualizing fall risk in older adults.

Falls result in severe physical as well as psychological impact among older adults. Beyond physical implications on injury-related trauma and in severe cases death, the psychosocial impact of falling can also be excruciating. Fear of falling can result in vicious cycles of decreased activity as well social isolation. These in turn lead to lower muscle strength and higher risk of future falls. In a similar manner, osteo-arthritis (OA) can lead to fear of movement (kinesiophobia) resulting in reduction of physical activity levels.

Within Work package 3 (WP3) of the BE-FIT study, we investigate real-world movement patterns and behaviour among vulnerable older adults (suffering from OA as well as at high risk of falling) when walking outdoors and in the neighbourhood. We acquire these movement patterns using the state-of-the-art inertial measurement unit sensors (wearables such as ZurichMOVE, WitMotion or Axivity). These sensors are equipped with triaxial accelerometers and gyroscopes and provide assessment of aspects such as impact and swing behaviour during different movements. In addition to movement, we examine participants physical activity rate and behaviour with eye-tracking glasses, video data, and continuous health monitoring sensors. The study addresses the following research questions:

1. What are the kinematic characteristics of walking among older adults with OA and/or previous falls in an ecological setting (neighbourhood)?

2. Do the kinematic characteristics of naturalistic walking predict physical activity rates?

3. Does the design of walkways (including overall layout, e.g. design of curbs, pathways etc and accessibility features e.g. size of the curbs, or height of side walk, ramps vs stairs) impact overall levels of physical activity as well as specifics of walking quality?

Job description

As a Research Assistant in the BE-FIT project, your work will directly contribute to developing new methods for long-term monitoring and understanding how environmental factors influence mobility and health. You will be responsible for:

ML Pipeline and Algorithm Development:

• Assist in evaluation and analysis of functional assessment data.
• Assist research personnel on developing new ML and algorithmic pipelines to extract features from wearable IMU sensors, GPS, eye-tracking glasses image data, and other sensors for both short and long term monitoring.
• Adapt processing pipelines for long-term monitoring data collected in naturalistic, real-world settings.
• Collaborate on data preparation for visualizations of collected data to explore, validate, and interpret the outputs of data processing pipelines.
• Conduct literature reviews and perform analysis on built environment and gait features.

Documentation & Reporting:

• Maintain organized records of data collection protocols, processing steps, and algorithm versions.
• Prepare regular progress reports on data collection, processing, and pipeline development, supported by clear visualizations.
• Contribute to the preparation of technical documentation, manuscripts, and presentations.
• Assist and participate in field data collection.

Mandatory Requirements

• Minimum a Bachelor's Degree, or equivalent in Data Science, Computer Science, Biomedical Engineering, or a related field.
• Proficiency in both Python and MATLAB.
• Strong foundation in both signal processing and machine learning.
• Excellent problem-solving skills and the ability to work effectively in a collaborative, interdisciplinary research setting.
• Good communication and interpersonal skills.

Desirable Attributes

• A proactive, self-starter attitude with the ability to work autonomously and take initiative.
• Strong organizational skills and attention to detail for managing complex datasets.
• Experience with wearable sensor data (e.g., IMU, GPS) and biomechanical analysis.
• Previous experience in managing and conducting field-based data collection.
• Previous experience with health-related data and research protocols.

Why SEC is your employer of choice?

• Adopted 5 Tripartite Standards by Tripartite Alliance for Fair & Progressive Employment Practices (TAFEP) Singapore.
• A diverse workplace with 32 nationalities, offering ample opportunities for mutual learning.
• Positive and inclusive working environment.
• 25 days of annual leave for fixed-term contracts.
• 1 day of Birthday Leave.
• Annual dental benefits.
• Committed to being a supportive employer as you prioritize your physical and mental wellness.
• Comprehensive healthcare insurance coverage.
• Flexible hybrid work arrangement (up to 2 days per week from home).
• Abundant networking opportunities across various disciplines.
• Accredited with NS mark certification.

The Singapore-ETH Centre is an equal opportunity and family-friendly employer. All candidates will be evaluated on their merits and qualifications, without regards to gender, race, age or religion.

Curious? So are we.

We look forward to receiving your online application with the following documents:

• Cover letter explaining your interest in the project and position.
• A comprehensive CV, specifically tailored to this position including previous projects in MATLAB and/or Python.
• Academic certificates and transcript of records.

Applications via email or postal services will not be considered.

Work location: 1 Create Way, CREATE Tower, Singapore 138602 (NUS University Town)

The salary will be commensurate with the candidate's experience.

Further information about the BE-FIT project can be found on our website: https://sec.ethz.ch/research/be-fit.html

For further information, please contact: Dr. Navrag Singh at navrag.singh@sec.ethz.ch