News
Knitted knee wearable may prevent or delay joint decline

A more sensitive, less bulky, knitted circuit-embedded knee wearable for wireless sensing of joint movement in real-time has been developed which could highlight early mobility issues – potentially preventing or delaying functional decline.
The stretchable knee wearable has been designed by a team from the Singapore University of Technology and Design, and if commercialised could have a massive impact on how age-related joint changes are dealt with both by medics and older patients.
Our bodies become stiffer and more inflexible as we age because we make less synovial lubricating fluid, which acts like oil to keep our joints moving smoothly. Our cartilage also becomes thinner and ligaments shorten and lose elasticity.
Mobility limitation – which is an early sign of functional decline – can manifest as muscle weakness, loss of balance, an unsteady gait, and joint pain. This in turn can lead to reduced physical activity, possible weight gain, decreased balance and coordination, and social and psychological effects.
It has long been known that long-term and continuous monitoring of joint motion may prevent or delay this decline by allowing the early diagnosis, prognosis, and management of mobility-related conditions.
This is usually made possible through either wearable or non-wearable engineered devices.
Non-wearable systems are reliable, but need a laboratory environment and trained individuals to use, monitor and interpret them, so are impractical for daily use. On the other hand, wearable systems are portable, cheaper, and much easier to use.
But typical wearable sensors tend to be inflexible and bulky.
A relatively new player are wearables made from soft, lightweight, malleable and non-invasive conductive fabric. They are comfortable to wear and can be used for long-term monitoring.
However, most conductive fabric-based wearables are prone to flagging up errors if they are removed from their intended location, and because they are attached externally to users’ clothing can also be cumbersome and restrictive.
The SUTD’s model boasts fewer external components, has more sensitive sensors, and because it’s made from a single section of highly stretchable fabric, allows the wearer more freedom.
The knee wearable has been developed by associate professor Low Hong Yee and her colleagues at the SUTD in collaboration with Dr Tan Ngiap Chuan of SingHealth Polyclinics. Their research has been published in the journal Advanced Healthcare Materials.
Professor Low said the team chose to concentrate on a wearable for the knee joint because of its importance for lower limb mobility.
But the team – which via the SUTD has filed patents related to the knee brace – is already looking to build on its work. The hope is to study the effect of sweat and humidity on sensor signals and to extend the research to include subjects from healthy and unhealthy populations in the future.
Professor Low said: “We have started working on extending the wearable to special user groups and to monitor other body joints, such as the shoulder. We’re also looking at securing an incubation fund to explore the commercialisation potential of the wearable.”
To develop the single-fabric circuit on the knee wearable, the team mechanically coupled an electrically conductive yarn with a dielectric thread of high elasticity in various stitch patterns.
Dimensions were customised according to the subject’s leg. The functional components – sensors, interconnects, and resistors – formed a stretchable circuit on the fully knitted wearable that allowed real-time data to be obtained.
However, putting together sensors, interconnects, and resistors in a single stretchable knit is difficult, as professor Low explained. “The synergy of yarns with different electrical and mechanical properties to achieve high signal sensitivity and high stretchability” was challenging, she said, as the desired properties for each component were vastly different.
Sensors need to produce a large change in resistance for enhanced sensitivity, while interconnects and resistors need fixed resistances of the highest and lowest values, respectively.

When worn, the knee brace converts changes in the knee joint to electrical signals, enabling the wireless and continuous real-time sensing of joint motion. Image: SUTD
As such, the researchers optimised yarn composition and stitch type for each component before connecting the functional circuit to a circuit board contained in a pocket of the wearable, allowing for wireless transmission of real-time data.
With a soft knee wearable developed, its components functional, and data transmission possible, it was time to test the performance of the wearable.
The team assessed the wearable through extension-flexion, walking, jogging, and staircase activities.
Subjects wore the knee wearable together with reflective markers that were detected by a motion capture system, allowing the comparison between sensor data and actual joint movement.
The sensor response time was less than 90 milliseconds for a step input, which is fast enough to monitor the human movements included in the study.
Additionally, the smallest change in joint angle that the sensors could detect was 0.12 degrees.
The research team say the potential impact of such a device in the medical field is huge.
Often, people ignore the early signs of mobility decline as they are not deemed serious enough to seek help. But wearable technology solves this problem by assessing a user’s mobility directly in real-time.
The team believe embedding a user-friendly sensor circuit into a soft and comfortable fabric may increase the public’s adoption of wearable technology – especially among the elderly.
Data can be gathered in real-time and translated into indicators that can detect mobility decline. When signs of mobility decline are found, preventive care, prognosis, and management of the healthcare condition can be given.
News
Mole rat gene extends mouse lifespan
News
AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
News
Vision implant firm raises US$230m
Wellness2 weeks agoInterview: The US company appealing Europe’s rejection of daily Alzheimer’s pill
News4 weeks agoLongevity startup Biopeak raises US$2.7m
News4 weeks agoBryan Johnson launches US$1m longevity programme
News4 weeks agoAgetech investment & innovation round-up
News4 weeks agoInterview: Dr Matthew Bennett on building resilience and a pain-free healthspan
News2 weeks agoCentenarians’ blood reveals longevity clues
News4 weeks agoRe:Cognition and Cera expand Alzheimer’s clinical trials access
News4 weeks agoFrench biotech raises €12m for osteoarthritis trial






















