Rehabilitation Research Institute of Singapore - RRIS, NTU

𝗚𝘂𝗶𝗱𝗶𝗻𝗴 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗖𝗮𝗿𝗲 𝗪𝗶𝘁𝗵 𝗜𝗻-𝗦𝗶𝗹𝗶𝗰𝗼 𝗥𝗲𝗵𝗮𝗯𝗶𝗹𝗶𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 (𝗦𝗣𝗠 𝟭𝗗) 𝗙𝗼𝗿 𝗨𝗻𝗶𝗹𝗮𝘁𝗲𝗿𝗮𝗹 𝗖𝗵𝗿𝗼𝗻𝗶𝗰 𝗛𝗲𝗺𝗶𝗽𝗮𝗿𝗲𝘁𝗶𝗰 𝗦𝘁𝗿𝗼𝗸𝗲 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀-𝗧𝗶𝗯𝗶𝗮𝗹 𝗔𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻

🚀 Advancing Clinical Care with Cutting-Edge Technology! 🌡️📊

Our project, "Guiding Best Practice Clinical Care with In-Silico Rehabilitation and Statistical Parametric Mapping (SPM 1D)," addresses challenges in assessing mobility post-stroke and in unilateral transtibial amputation cases. Traditional methods are often subjective and lack sensitivity to track subtle improvements during rehabilitation. How can we introduce more objectivity and precision into these evaluations?

Oᴜʀ Pʀᴏᴘᴏꜱᴇᴅ Sᴏʟᴜᴛɪᴏɴ 🌐
We propose objective clinical gait analysis using modern technology. Motion capture systems are employed and movement analytics to evaluate the mobility and gait of both stroke survivors and amputees, striving for a more data-driven approach. Our objectives include:

① Collecting 3D Motion Capture (MOCAP) data from two distinct groups: chronic hemiparetic stroke patients and unilateral transtibial amputees.

② Analysing and comparing this MOCAP data with anonymised reference data from the local healthy RRIS Ability Data set.

③ Collaborating with the NTU RRIS team to develop a clinician-centric SPM1D clinical analysis framework, enabling objective analysis of pathological gait Movement Proficiency Index (MPI) for conditions like stroke and transtibial amputations. This framework will provide insights into movement proficiency at both whole-body and isolated joint levels.

Cᴜʀʀᴇɴᴛ Pʀᴏɢʀᴇꜱꜱ 📈
We successfully completed the study in 2022, involving 15 chronic stroke survivors and 15 amputees at the RRIS gait laboratory. Participants performed lower-limb tasks under clinician supervision, with all trials completed within 2.5 hours. We meticulously labeled and pre-processed MOCAP datasets and conducted group analyses. Our primary focus is on leveraging MOCAP and movement analytics for objective gait analysis. Additionally, we've developed a user-friendly Python-based interface called "MovementRx" to visualise joint trajectories. We are in the midst of publishing our work in a clinical journal😉

🚶🏼‍♂️🚶🏻‍♂️Get ready to meet the incredible squad of researchers who turned this groundbreaking research project into a reality! Dr. Cyril John William DONNELLY, Dr Ananda SIDARTA.

Want to know more? Go ahead, have a read ⏩https://bit.ly/RRISSPM1D

🍂𝗞𝗻𝗶𝘁𝘁𝗲𝗱 𝗪𝗲𝗮𝗿𝗮𝗯𝗹𝗲 𝗦𝗲𝗻𝘀𝗼𝗿𝘀 𝗳𝗼𝗿 𝗛𝘂𝗺𝗮𝗻 𝗟𝗼𝗰𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗦𝗲𝗻𝘀𝗶𝗻𝗴 & 𝗙𝗮𝗹𝗹 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻🍂

𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆!👩‍⚕️🌐

With the rising demand for healthcare assistance due to an ageing population globally, we're witnessing a shift towards more cost-effective and remote healthcare solutions. Leveraging the power of IoT, wireless communication, cloud computing, and textile-based technologies, we can now enable remote monitoring and data-driven treatment methods for patients.

An innovative solution on the horizon is the use of knitted wearable sensors for human locomotion sensing and fall detection. These sensors, made from flexible and comfortable textile materials, have great potential to bridge the gap in monitoring joint health outside of clinical settings. Their piezoelectric properties allow for seamless integration into everyday apparel, ensuring unhindered movement and preserving personal privacy.

A pilot study is underway to explore the application of commercial wearable smart devices in conjunction with the knitted knee brace from the Singapore University of Technology and Design (SUTD). This study focuses on collecting long-term human motion data in the senior population, examining how daily activities impact joint health over time. Participants will engage in activities of daily living (ADLs) and simulated falls, with their movements recorded by a motion capture system as the reference standard. Additionally, invasive bone-pin comparisons using cadaver specimens will be made, and readings from the wearable textile sensors will be compared against these benchmarks.

🚶🏽‍♂️🚶🏻‍♀️𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝘁𝗲𝗮𝗺 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝘄𝗵𝗼 𝗯𝗿𝗼𝘂𝗴𝗵𝘁 𝘁𝗵𝗶𝘀 𝘃𝗶𝘀𝗶𝗼𝗻𝗮𝗿𝘆 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗹𝗶𝗳𝗲!
Project Lead: Dr. Lau Junliang, Site Researchers: Mohamed Ahshik, Josephine Lam, Site PI: Dr. Ananda Sidarta

📜✏️𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲? 𝗚𝗼 𝗮𝗵𝗲𝗮𝗱, 𝗵𝗮𝘃𝗲 𝗮 𝗿𝗲𝗮𝗱:
https://www.ntu.edu.sg/rris/research-focus/research-projects/knitted-wearable-sensors-for-human-locomotion-sensing-fall-detection

🚶🏻🚶🏻‍♂️🚶🏻‍♀️𝗖𝗮𝗽𝘁𝘂𝗿𝗶𝗻𝗴 𝗠𝗼𝗺𝗲𝗻𝘁𝘀 & 𝗠𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 - #𝗥𝗥𝗜𝗦 𝗔𝗯𝗶𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲🚶🏻🚶🏻‍♂️🚶🏻‍♀️

Traditionally, patient mobility rehabilitation has been seen as more of an art than a science due to the lack of scientific data guiding recovery predictions or explaining treatment variations among patients. While Western studies have provided valuable insights since the 1980s, they haven't addressed Asia-specific mobility challenges.

RRIS' innovative approach involves a marker-based motion capture system, capturing 6 upper and 6 lower limb movement tasks derived from standard rehabilitation assessments. We now have data from more than 650 subjects.

This normative group becomes the foundation basis for objectively identifying movement deficits related to conditions such as stroke, knee osteoarthritis, and amputation. Harnessing big data techniques for analysis, our goal is to extend the Ability Data initiative beyond Singapore, opening new opportunities in the field.

As of December 2023, RRIS’ Ability Data stands as the world's largest normative database for objective human movement data.

🍎Interested to join us as a participant? Contact us at:
Email: [email protected]
Tel: 9295 6984/9862 8249🍏

🔥 𝗙𝗶𝗿𝗲𝘀𝗶𝗱𝗲 𝗖𝗵𝗮𝘁 𝘄𝗶𝘁𝗵 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁, 𝗜-𝗟𝗶𝗻𝗴 𝗬𝗲𝗵! 🔥
Today, we had the privilege of sitting down with our very own research scientist, Asst. Prof. I-Ling Yeh, to learn more about her inspiring journey and the groundbreaking work she's leading at .

🌍 𝙏𝙝𝙚 𝙅𝙤𝙪𝙧𝙣𝙚𝙮 🌍
I-Ling’s journey in occupational therapy began at National Taiwan University, leading to a Master's Degree specialising in stroke rehabilitation. She is currently an Assistant Professor in the Health and Social Sciences Cluster at the Singapore Institute of Technology (SIT).

🌟 𝘾𝙤𝙡𝙡𝙖𝙗𝙤𝙧𝙖𝙩𝙞𝙣𝙜 𝙬𝙞𝙩𝙝 𝙍𝙍𝙄𝙎 🌟
I-Ling's role at RRIS is centered on the development and validation of instruments designed to assess movement indicators. Recognising that clinical assessments can sometimes rely heavily on subjective observations, her work is pivotal in bringing objectivity and precision to the field. Her expertise in clinical biomechanics and neurorehabilitation ensures that RRIS' research projects have clear clinical relevance and the potential for a meaningful impact.

🚀 𝙋𝙧𝙤𝙟𝙚𝙘𝙩 𝙂𝙤𝙖𝙡𝙨 𝙖𝙣𝙙 𝙏𝙞𝙢𝙚𝙡𝙞𝙣𝙚 (𝙀𝙣𝙙 2024) 🚀
The project team, led by I-Ling, has set ambitious goals. By the end of 2024, they aim to complete the recruitment of clinical populations, a crucial step in validating the instruments and indicators they are developing. This validation process ensures that their work will have a profound and positive impact on the field of healthcare and rehabilitation, paving the way for more effective intervention planning.

Thank you once again, I-Ling, for sharing your remarkable journey and the exciting work happening at RRIS!

𝗚𝘂𝗲𝘀𝘀 𝗠𝗲!🕵🏻‍♀️

𝘈𝘯𝘴𝘸𝘦𝘳 𝘙𝘦𝘷𝘦𝘢𝘭: The Human Robot Interface (HRI) Toolbox🪄

😏Did you get it right?

The HRI Toolbox is the unsung hero behind RRIS’ innovations. Our researchers understand the importance of software for value capture and have been hard at work, gearing towards the ultimate goal in creating a robust, modular & reusable software ecosystem.

We adopt industry best practices, such that the research software is more vigorous, reliable and ultimately implemented across RRIS’ robotics.

🕵️🔍 𝗪𝗵𝗮𝘁 𝗮𝗺 𝗜? 🤔

Answer Revealed: The answer is Mobile Robotic Balance Assistance (MRBA), a cutting-edge technology that combines wearable sensors and intelligent algorithms to detect potential falls in the elderly before they occur.

Did you guess it correctly? Comment down below and let us know!