Low-cost, Sensor-equipped Insole Developed to Monitor Gait of Patients with Mobility Impairments

 Ask your competent? doctor if this is better than all these earlier ones! And when monitoring is done are THERE EXACT PROTOCOLS TO FIX THE PROBLEMS?

  Sensoria™ Fitness Socks March 2014

Rapid Rehab Smart Insole Will Train Athletes and Assist Rehab Patients June 2013

 Design of a biofeedback device for gait rehabilitation in post-stroke patients August 2015

Or this:

 A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole  2016

Or this:

A Reliability Study of the Load Distribution Percentage While Walking Using Curalgia Feet Sx Smart Insoles August 2024 

The latest here:

Low-cost, Sensor-equipped Insole Developed to Monitor Gait of Patients with Mobility Impairments

AMHERST, Mass. – An interdisciplinary team of University of Massachusetts Amherst researchers has developed a low-cost shoe insole, equipped with force-sensitive resistors (FSR), that aims to improve the management of health conditions that impair mobility, such as stroke, Parkinson’s disease and osteoarthritis.

The insole “measures two important kinetic parameters that are relevant to how people walk; that is, the ground reaction force (GRF) and center of pressure (CoP),” says lead investigator Sunghoon Ivan Lee, assistant professor in the Manning College of Information and Computer Sciences. “Those parameters contain very important information, especially for people who have gait problems.”

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UMass Amherst lead author Brandon Oubre

Lee’s 2018 National Institutes of Health Trailblazer Award for Young Investigators helped fund the research, published recently in the journal IEEE Transactions on Biomedical Engineering and selected as a Featured Article. In addition to lead author Brandon Oubre, an information and computer sciences Ph.D. candidate, Lee collaborated with kinesiologist Katherine Boyer, a gait expert, and Ph.D. candidate Skylar Holmes, both from the School of Public Health and Health Sciences.

Information from the insole sensors could help clinicians conveniently monitor disease progression over time and make earlier interventions for people with neurological, musculoskeletal and other conditions that affect mobility.       

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Currently these gait parameters can only be measured in a lab with expensive scales and specialized cameras that track reflective “dots” attached to the body. “It’s not easily accessible for people, especially those living in rural or underserved areas, so it’s difficult to monitor how these parameters evolve throughout the course of their therapy or rehabilitation,” Lee says. “And the lab environment does not really represent how they walk naturally outside of the laboratory.”

The researchers’ challenge was to come up with an inexpensive, wearable solution to accurately measure where the pressure is centered on the sole when a person walks.

Image

“For healthy individuals, there’s a normal way to put the pressure on the ground when you walk: You hit with the heel and then you will roll around the outer edge of your sole, then your toes will be in contact with the ground and then finally you take off,” Lee says. “But these inexpensive sensors are not very accurate, that’s the caveat.”

So the research team built an artificial-intelligence algorithm to process the sensors’ data, resulting in accurate information about the kinetic parameters. “That’s the key contribution of our paper,” Lee says.

The research found that the insole devices accurately estimated GRF and CoP based on models, despite the deficiencies in force sensitive resistor data.

One day, the researchers hope that insoles with sensors could be used in clinics and patients’ home environments to provide real-time data about their walking gait, sent wirelessly to health care providers.

“There will be many hurdles to enable this vision, but that’s the ultimate thing that we’re aiming for,” Lee says.

Wearable Stroke Rehabilitation Device Gives Patients the Upper Hand

 Your mentors have told you that monitoring is useless unless you have EXACT RECOVERY  PROTOCOLS mapped to the deficits shown! NO? Find better mentors that know something about stroke recovery, not just fairly useless monitoring! And did your mentors show you all the wearables out there? Or were they incompetent in not knowing of them?

• wearable sensors(35 posts to January 2018)

Wearable Stroke Rehabilitation Device Gives Patients the Upper Hand

A team led by University of Massachusetts Amherst researchers has developed a wearable wrist device powered by a machine-learning algorithm that can continually track changes in arm movement impairment caused by strokes. Monitoring changes throughout the entire rehabilitation process will allow clinicians to make real-time adjustments to therapy programs for tailored interventions and personalized care instead of the one-size-fits-all current standard. 

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Ivan Lee (left) and Ryan Wang

“We are the first group to actually show that, using wearable data, we can extract information about patients’ motor severity, which clinicians can actually use to determine whether their intervention is effective or not,” says Sunghoon Ivan Lee, associate professor in the Manning College of Information and Computer Sciences at UMass Amherst and corresponding author on the paper describing this new technology. The research was completed alongside colleagues from Washington University in St. Louis, Shirley Ryan AbilityLab and Harvard Medical School/Mass General Brigham.

Annually, more than 795,000 Americans experience strokes, with upper-limb mobility issues affecting up to 77% of patients immediately following the event. About 40% of patients continue to have chronic issues, posing a major limitation to independent living.

While physical therapy improves these mobility issues, it is not without its limitations. Currently, progress is measured by a clinician’s observational assessment, which takes about 30 minutes. Because this is a time-consuming process, assessment typically occurs only pre- and post-rehabilitation.

“That means during that therapy process, neither the patient nor the therapist has a clear idea of how patients are responding to the treatments that they’re receiving,” says Lee. “Currently, clinicians aren’t able to see if patients are responding to the prescribed exercises, and patients have no way of knowing how they are progressing.”

With a wearable monitor, recovery data collection can be ongoing and occur outside of therapy sessions, allowing the therapist to make timely adjustments to treatment strategies for truly personalized rehabilitation.

Also, tracking movement in a patient’s real-life environment may be more indicative of true performance, as opposed to movement that is artificially produced in a clinic. The wrist-wearable also captures movement at all times of day, versus at just one snapshot of time. And finally, patients may find that tracking their own recovery progress increases their engagement in the therapy practices and keeps them motivated.

Lee is optimistic that this increased transparency will translate to improved therapy outcomes.

An accelerometer sensor in the device captures upper-limb movement, which is then interpreted by a machine-learning algorithm developed by Lee and his graduate student and the lead author on the paper, Ryan Wang.

“[Movement and impairment severity] are related because the less severe you are, the more likely you’re going to move a lot, but they’re not exactly the same,” says Lee. “Increasing the use of the limbs—yes, we can encourage the person to make use of the limb more. But patients cannot make instant changes to motor severity through short-term behavior change.”

Their model, described in Science Translational Medicine, was trained on accelerometer data and clinician assessment scores of subacute (one week to six months after a stroke) stroke patients and healthy individuals. They found that their algorithm was 40-50% more accurate—meaning a better reflection of the patient’s true condition—compared to clinician evaluation.

In addition to the clinical application of using the device to inform personalized interventions, Lee’s work demonstrated that the device has a strong research application. In recreating a previous study using their own digital biomarker instead of clinician observations, the researchers found that they generated statistically significant results with 50% fewer participants.

“We can get a clear idea of the effectiveness of the intervention using a lower sample size and far fewer resources,” says Lee. This can expedite the research process and reduce costs.”

This work was supported by the National Institutes of Health. With a provisional patent filed, Lee is pursuing commercialization through the startup Lumid Health with support from UMass Amherst Institute for Applied Life Sciences’ Translational Seed Award, the UMass Office of Technology Commercialization & Ventures’ (OTCV) Technology Development Fund and participation in the NSF I-Corps Training Program.

To further develop the technology, Lee’s research partners are currently recruiting stroke patients for a study held at the Spaulding Rehabilitation Hospital in Boston. Those interested in participating can see the inclusion criteria and apply here.

Durable, Breathable, and Sweat-Resistant Nanocrack-Based Fiber Strain Sensors for Joint Monitoring in Elderly Stroke Rehabilitation

I can really see no use for this, monitoring stroke rehab DOES NOTHING FOR RECOVERY! 

Unless this monitoring leads to EXACT PROTOCOLS to recover from the deficits in your joint movements, I can't see much use for this.

Durable, Breathable, and Sweat-Resistant Nanocrack-Based Fiber Strain Sensors for Joint Monitoring in Elderly Stroke Rehabilitation

 Joint Monitoring in Elderly Stroke Rehabilitation

• Xinxin Zhang
• Dongxing Lu
• Huihui Xu
• Zhengtong Song
• Xiuming Cao
• Yanhong Cao
• Yong Xu
• Qufu Wei
• Qingqing Wang*

Access Through Your Institution

Other Access OptionsSupporting Information (3)

Abstract

Flexible fiber-based strain sensors show great promise for joint motion monitoring in stroke rehabilitation and elderly care. However, the rational design of low-cost sensors that simultaneously offer high sensitivity, excellent stability, and practical applicability is still a great challenge. In this study, multiwalled carbon nanotubes were incorporated into thermoplastic polyurethane to fabricate a coaxial fiber structure with crack effects via wet spinning. By adjusting the extrusion speed ratio between the core and sheath layers, the thickness of the fiber shell was optimized and a fine crack network was formed, enhancing both sensitivity and mechanical properties. Experimental results show that the fabricated fiber sensor exhibits a high sensitivity (strain range: 70–175%, gauge factor = 3.154), with a wide detection range (250% strain), an ultralow detection limit (<0.1%), and excellent cyclic durability (>2000 cycles). The sensor can be effectively applied to monitor human joint movements. Meanwhile, the nanocrack-based fiber sensor (NFS) exhibits excellent photothermal characteristics, strong resistance to sweat and washing, and good breathability (981.9 mm/s). Notably, the NFS enables real-time monitoring of physiological movements with Bluetooth data transmission. Furthermore, its localized photothermal effect can promote blood circulation, providing additional therapeutic value in stroke rehabilitation. These features highlight the great potential of NFS sensors in smart healthcare and wearable health technologies.

© 2025 American Chemical Society

Subjects

Post-stroke Gait: Implications for Future Customizable Rehabilitation ApproachesCustomizable Rehabilitation Approaches

Unless this monitoring leads to EXACT PROTOCOLS to recover from the deficits in your walking, I can't see much use for this.  The mentors failed this research fellow by not specifying the proper outcome.

 Post-stroke Gait: Implications for Future Individuals with Post-stroke Gait: Implications for Future Customizable Rehabilitation Approaches Customizable Rehabilitation Approaches

To the Graduate Council:
I am submitting herewith a dissertation written by Azarang Asadi entitled “Motor
Control Quantification and Necessary Improvements for Individuals with Post-stroke
Gait: Implications for Future Customizable Rehabilitation Approaches.” I have
examined the final electronic copy of this dissertation for form and content and
recommend that it be accepted in partial fulfillment of the requirements for the degree
of Doctor of Philosophy, with a major in Biomedical Engineering.
Jeffrey A. Reinbolt, Major Professor
We have read this dissertation
and recommend its acceptance:
Zhenbo Wang
Emre Demirkaya
Michael A. Langston
Accepted for the Council:
Dixie L. Thompson
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)Motor Control Quantification and
Necessary Improvements for Individuals with Post-stroke Gait:
Implications for Future Customizable Rehabilitation Approaches
A Dissertation Presented for the Doctor of Philosophy Degree
The University of Tennessee, Knoxville
Azarang Asadi
May 2024© by Azarang Asadi, 2024
All Rights Reserved.
To my mother, Mahbobeh, for all the support and sacrifices, and my sister, Mehregan,
for always believing in me.
Acknowledgements
I would like to thank and express my utmost gratitude to my advisor, Dr. Jeffrey
A. Reinbolt. His guidance and mentorship made this work possible and carried me
through all stages. His continuous support and patience made me feel competent,
and I am eternally grateful for being a graduate student in his research group.
I would like to thank the members of my dissertation committee, Dr. Michael
Langstong, Dr. Zhenbo Wang, and Dr. Emre Demirkaya for their time and valuable
comments. Their critical feedback and knowledgeable insights challenged me to
become a better scientist.
My appreciation extends to my fellow colleague at Reinbolt Research Group,
especially Ashley Rice who helped me tremendously with OpenSim and MATLAB.
I would like to extend my deepest gratitude to my mother. Mom, your selflessness
and sacrifices have been the bedrock of my academic journey. This accomplishment is
as much yours as it is mine, and I dedicate it to you with all my love and appreciation.
I am profoundly grateful to my sister whose enduring support, encouragement,
and belief in me have been a constant source of strength throughout my academic
journey. Her unwavering faith in me has been instrumental in achieving this milestone
and I am deeply thankful for her love and support.
I would like to thank my family and friends, especially Minou Attaei. Her support
and encouragement throughout this journey have been invaluable, and I am very
fortunate to have such a wonderful friend. Heartfelt thanks to my friend, Nobahar
Shahidi, for the support and guidance. I am grateful to have such a caring friend.
ivLast but not the least, I would like to give special thanks to my sweet dear feline
companions, Luna and Mah Banoo. Without their emotional support, none of this
work would have been possible. To my beloved cats, thank you for being my furry
writing companions throughout this journey.

Abstract

Although often taken for granted, walking is an extremely complex motor skill that
requires sensory inputs, neural communication, advanced control strategies, and
coordination of the muscles and joints. Electrical signals traveling from the brain
to the muscles are transformed to mechanical forces to achieve desired motion. A
stroke damages the central nervous system and neural pathways, limiting the ability
of survivors to walk. Walking speed is significantly decreased and asymmetrical
walking patterns emerge. A crucial component of stroke rehabilitation is gait training,
a therapeutic intervention to help individuals to improve their walking ability, as
walking is essential for functional independence and long-term survival.
Walking speed is often used as a gold standard for assessing the walking
capabilities of stroke survivors, however, it’s important to note that a higher walking
speed may not always indicate true recovery and may be a result of compensatory
mechanisms. Monitoring the neurological impairment can improve our understanding
of the walking disorder associated with stroke, guide the treatment according
to patient’s specific needs, and contribute to development of new rehabilitation
paradigms to improve the neuromuscular impairment.
In this work, we aim to establish a computational framework for real-time
monitoring of walking ability of stroke survivors at a neural level, applicable for
both gait laboratories and real-world settings. Additionally, we will investigate
the capability of using such framework to improve the rehabilitation techniques for
maximizing motor control complexity. We unite biomechanical modeling, simulations,
vistatistics, and machine learning to achieve the goals of this research. First,
we will investigate various quantitative measures of walking to understand their
association with neurological impairment, and assess their potential for neuromuscular
impairment monitoring purposes. Second, we will examine the utility of wearable
sensors for assessing motor control complexity of stroke survivors during walking, with
the aim of making assessments accessible beyond the gait laboratory. Lastly, we will
investigate the muscle activity changes corresponding to motor control improvements
of stroke survivors, in order to identify new rehabilitation paradigms to enhance the
motor control complexity of post-stroke gait.

Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body

 If there were ANY FUNCTIONING BRAINS at all in the stroke medical world this would be looked at as a godsend. You could monitor the neuronal cascade of death as it occurs so you could see which interventions work to save neurons from dying! But we have NO STROKE LEADERSHIP! So you'll continue to be screwed until we get survivors in charge! So start screaming at your incompetent doctors and hospital! Everything in stroke is a complete shitshow!

Send me hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and my response in my blog. Or are you afraid to engage with my stroke-addled mind?  Survivors would like to know why you are doing nothing to actually solve stroke. NO excuses allowed,  IT IS YOUR FUCKING JOB TO SOLVE STROKE! GET THERE!

Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body

• De-Shaine Murray*
• , 
• Laure Stickel*
• , and 
• Martyn Boutelle
• 
  

Cite this: ACS Chem. Neurosci. 2023, 14, 19, 3599–3608

Publication Date:September 22, 2023

https://doi.org/10.1021/acschemneuro.3c00063

Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0.

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SUBJECTS:

• Computational modeling,
• Computer simulations,
• Diffusion,
• Membranes,
• Probes

ACS Chemical Neuroscience

Abstract

Real-time measurement of dynamic changes, occurring in the brain and other parts of the body, is useful for the detection and tracked progression of disease and injury. Chemical monitoring of such phenomena exists but is not commonplace, due to the penetrative nature of devices, the lack of continuous measurement, and the inflammatory responses that require pharmacological treatment to alleviate. Soft, flexible devices that more closely match the moduli and shape of monitored tissue and allow for surface microdialysis provide a viable alternative. Here, we show that computational modeling can be used to aid the development of such devices and highlight the considerations when developing a chemical monitoring probe in this way. These models pave the way for the development of a new class of chemical monitoring devices for monitoring neurotrauma, organs, and skin.

This publication is licensed under

CC-BY 4.0.

• 
• 

KEYWORDS:

• neuromonitoring
• computational modeling
• microdialysis
• neurotrauma
• chemical
• analytical

 Special Issue

Published as part of the ACS Chemical Neuroscience special issue “Monitoring Molecules in Neuroscience 2023”.

Introduction

ARTICLE SECTIONS

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Analytical chemical monitoring devices are an important way of studying diseased and injured tissue. They typically involve a sampling element, often a commercial microdialysis probe, coupled to a microfluidic manifold that incorporates relevant chemical sensors. Devices of this nature are finding increasing utility due to improvements in real-time, continuous, chemical monitoring and advancements in sampling tissue and organs, such as the brain, by less invasive means. This paper describes the design and development, using the aid of computational modeling, of a new class of sampling device that incorporates tissue sampling directly into the sensor-containing manifold.

The successful sampling and monitoring of tissue extracellular fluid (ECF) can provide vital information for clinicians. (1) Chemical levels that deviate from typical values in the blood and ECF can be useful indicators of the proliferation of disease, degeneration, and damage. (2) The accepted standard for chemical monitoring is the sampling of blood, which requires the repeated removal of small samples for offline analysis using high performance liquid chromatography (HPLC) or mass spectrometry (MS). This analysis can be time-consuming and often misses dynamic changes that occur at faster time scales, for example during the acute progression of diseases. (3)

Accurate detection of patient deterioration by quickly elucidating patterns in disease and injury progression can be achieved by online, real-time measurement facilitated by integrated microfluidic channels within analytical chemical devices. (4) Three main approaches can be employed to achieve continuous monitoring: (1) Implanted electrodes with modified surfaces for chemical transduction through biosensing; (5) (2) optical devices that sense in close proximity to the media in question─using light sources; (6,7) and (3) sampling devices such as microdialysis probes which deliver samples representative of ECF for ex-vivo analysis. (5) Of these methods, implanted electrodes often suffer from disturbances of the electrode surface that lead to drift, ultimately reducing the measurement accuracy and precision over time. (8)

Conversely, optical methods, although noninvasive and thus the most desirable, often lack sufficient sensitivity to provide reliable reporting of dynamic changes in chemical concentrations. (9) Therefore, implantable microdialysis probes that act as delivery devices for analytical equipment outside of the body provide a middle ground between the aforementioned methods. (10) When microdialysis is coupled with microfluidics and linked to ex-vivo biosensors, continuous monitoring of dialysate can be achieved. (11) A distinct advantage here is all sensing apparatus is found outside of the body and thus readily accessible and can be easily replaced when performance issues are observed. (12) This has been shown with the development of continuous online microdialysis (coMD) (13,14) which displays data at 200 samples per second, with a slight offset delay.

However, the probes that afford this analysis are concentric in nature and penetrate tissue directly to establish a diffusional concentration gradient. On the order of a few 100 μm, such diameters create penetration injuries, which lead to inflammation and the development of barriers of tissue that can surround and hamper the sampling probe. (15,16) The retrodialysis of dexamethasone, a glucocorticoid anti-inflammatory, has been employed by Varner et al, to alleviate such barriers by reducing the proliferation of abnormal tissue and thus enhancing the level of detection at the sampling zone. (17,18) However, such pharmacological interventions can be completely avoided by designing surface probes that do not have to be inserted into the cortex and as such do not create penetrative injuries upon placement. It is worth noting that such devices can only currently be utilized when a craniectomy or craniotomy has occurred but the “softness” of these prospective technologies means that surface probes could be folded and introduced in minimally invasive ways, such as through a burr hole, before being unraveled. (19,20)

Surface microdialysis (s-μD) is not a new concept. Foundational work from Abrahamsson, Akesson, and colleagues led to the development of a probe that can be sutured to the heart, bowel, liver, and other tissue. (21−23) This device is now commercially available and is known as the OnZurf probe. The devices that follow the general principles described here build on this work but incorporate soft, flat, and flexible materials and a different form factor. Such materials allow for the incorporation of flexible electronics using soft lithography and the possibility of developing new surgical protocols to reduce the risk of surgery on a very fragile region of the body. (20)

By extension, such devices can also make for useful environments to support cell growth, monitoring, and the development of cellular layers into organs, for organ-on-a-chip (OOAC) and organ-in-a-chip (OIAC) applications. The use of semipermeable membranes can allow for the delivery of nutrients to a tissue chamber and the corresponding microfluidics can be used for rapid chemical stimulation. (24,25) Therefore, the development of soft, flexible, near 2-dimensional sampling microdialysis probes solves many immediate issues with chemical monitoring of tissues and has a wide applicability that spans multiple subfields of bioengineering. Such devices open the possibility of monitoring tissues on different scales:

• In-vivo monitoring could be easily implemented to assess the brain after trauma, with thin, conformal and biocompatible devices being placed directly on the surface of the brain under the dura. Less damage would be sustained to the tissue during implantation, reducing the foreign body response (FBR) and increasing the viability and longevity of the implanted device.

• Ex-vivo monitoring would benefit from the use of such devices. For example, the chemical state of a kidney could be continually monitored for health and function in transit, without penetrative injuries. (26) As transplant organs are very sensitive, preserving their integrity while getting clear updates about their health (with minimal damage) would be essential to performing successful transplant operations.

• OOAC and OIAC experiments could be developed using these devices where tissue slices could be placed in close proximity to, or be directly embedded within, the device. Using the microfluidic characteristics of the analytical chemical device, the conditions and delivery of nutrients these tissues would need to survive could be mimicked, in addition to the simulation of disease states. (27−29)

• Skin monitoring using such noninvasive, conformal devices could monitor the composition of sweat on the surface of the skin. (30) Consumer health, such as fitness monitors or continuous glucose monitoring for diabetes, could benefit from such form factors.

Current fabrication methods offer great freedom of design; therefore, highlighting the critical parameters and design features for effective chemical sampling using microfluidics will greatly aid the development of efficient prototypes. One way of ascertaining these critical parameters is by constructing possible prototypes using computational models. Applying modeling to microfluidic geometries to assess the fluid dynamics and performance of a system is a quick way to home in on feasible, real-world solutions. (31) Such modeling has already been utilized within this field to investigate drug delivery using retrodialysis and tissue damage from low-flow perfusion devices. (32−34) Computational modeling is therefore becoming an increasingly useful tool that allows for the iterative evaluation, development, and optimization of microfluidic and microdialysis systems. (35)

One such computational modeling environment is COMSOL Multiphysics. This is an interactive environment that can be used for solving a range of scientific and engineering questions. COMSOL works on the basis that partial differential equations form the basis of fundamental scientific laws. Within the software, models can be built that simulate physics phenomena by combining these partial differential equations, without the need for an in-depth knowledge of mathematics. A key differentiator of COMSOL is the ability to model and investigate multiple phenomena at the same time, which is more indicative of real-world scenarios, where multiple variables can impact the performance of your system. (36) However, users of such software should be aware that such tools should only be used as a guide for their experimental counterparts and are, more often than not, based on assumptions and simplifications. In addition, although time can be saved by using modeling such as COMSOL, there is a computational cost, and this increases with the complexity of the model that is created. With the increase in computational power available at a low cost, we can also go beyond modeling simple fluidic components as circuit analogies or numerical solutions and generate large data sets without conducting physical experiments. For even more complex systems, where a system cannot be adequately modified from first-principles, machine learning can be utilized to probe complex microfluidic behavior. (37,38)

In this paper, we show how modeling can be implemented, in order to optimize the prototyping of near-2D sampling devices. We consider the separation of consecutive signals, multiple sampling points, and changing the geometry of channels in order to ascertain key considerations for robust chemical sampling with high resolution and minimal time delay, with potential applicability to neuromonitoring.

 

More at link.