Posts Tagged machine learning

[WEB SITE] Personal Rehab and Recovery Through Virtual Therapy

Virtual therapy is based on research that combines leading-edge data techniques with wearable robotics, artificial intelligence and machine learning.

An engineering researcher from New Zealand’s University of Auckland has been awarded a Rutherford Discovery Fellowship.

The Associate Professor, who is developing a virtual therapy technology for personal rehabilitation, is one of eleven Fellows for 2019. The Fellowship provides NZ$ 800,000 in funding over five years.

According to a recent press release, his research combines leading-edge data techniques with wearable robotics, artificial intelligence (AI) and machine learning.

The aim is to create devices that are capable of personalising rehabilitation and recovery plans, which are cheaper and more efficient than humans.

The Problem for Personal Rehabilitation

  • Currently, rehabilitation after a medical event, such as stroke, is carried out by trained physical or occupational therapists.
  • However, much of the work is physically demanding and the cost is relatively high and time-consuming.
  • While some robotics devices used for physical rehabilitation have been developed overseas, they lag far behind what a human therapist is capable of.
  • The current technology has little or no intelligence and can only act on predefined rules. Thus, it is not tailored to individuals and does not have the ability to adapt and learn as a human therapist would.

The Solution for Personal Rehabilitation

  • The researcher’s work, meanwhile, takes a strongly data-driven approach, looking at the fundamental physiology of human movement.
  • It will build on that information in order to create individual recovery plans that take into account the effects of a diverse range of physical impairments.
  • The goal is to make real progress towards creating low-cost robotic ‘virtual therapists’ with the ability to deliver automatic but very precise treatments.
  • The Rutherford Discovery Fellowships, managed on behalf of the government by the New Zealand Royal Society Te Apārangi, aim to attract and retain talented early- to mid-career researchers by helping them establish a track record for future research leadership.
  • The high costs of healthcare not just in New Zealand but around the world mean that progress in the area of medical technologies and personalised therapies and treatments needs to be prioritised.

Stressbuster

In other news, the University was the site of a unique digital treasure hunt recently to mark Stress Less Week.

Stress Less week was held 7 to 11 October as thousands of students prepare to head into study break and exam period.

A student start-up developed the technology used in the app-based game, which challenged the students to unlock and solve riddles on the City Campus to find secret locations and discover rewards.

The start-up’s Founder explained that fun is the ultimate antidote to stress.

They provided an experience that facilitated getting out and connecting with peers, before it gets too close to exams and after the mid-semester wave of assignments.

They are passionate about using new technologies to turn cities into playgrounds, developing a portfolio of technologies in the process.

These technologies include holograms, face-recognition software and transparent glass screens, which they draw on to design interactive games.

Using the campus for a big treasure hunt is a great way to test the waters before thousands of dollars are put into more commercial ventures, and scale-up the app to use in different situations.

 

via Personal Rehab and Recovery Through Virtual Therapy

, , , , ,

Leave a comment

[WEB SITE] AI helps identify patients in need of advanced care for depression

Depression is a worldwide health predicament, affecting more than 300 million adults. It is considered the leading cause of disability and contributor to the overall global burden of disease. Detecting people in need of advanced depression care is crucial.

Now, a team of researchers at the Regenstrief Institute found a way to help clinicians detect and identify patients in need of advanced care for depression. The new method, which uses machine learning or artificial intelligence (AI), can help reduce the number of people who experience depressive symptoms that could potentially lead to suicide.

The World Health Organization (WHO) reports that close to 800,000 people die due to suicide each year, making it the leading cause of death among people between the ages of 15 and 29 years old.

Major depression is one of the most common mental illness worldwide. In the United States, an estimated 17.3 million adults had at least one major depressive episode, accounting to about 7.1 percent of all adults in the country.

Image Credit: Zapp2Photo / Shutterstock

Image Credit: Zapp2Photo / Shutterstock

Predicting patients who need treatment

The study, which was published in the Journal of Medical Internet Research, unveils a new way to determine patients who might need advanced care for depression. The decision model can predict who might need more treatment than what the primary care provider can offer.

Since some forms of depression are far more severe and need advanced care by certified medical health providers, knowing who is at risk is essential. But identifying these patients is very challenging. In line with this, the researchers formulated a method that scrutinizes a comprehensive range of patient-level diagnostic, behavioral, and demographic data, including past clinic visit history from a statewide health information.

Using the data, health care providers can now build a technique on properly predicting patients in need of advanced care. The machine learning algorithm combined both behavioral and clinical data from the statewide health information exchange, called the Indiana Network for Patient Care.

“Our goal was to build reproducible models that fit into clinical workflows,” Dr. Suranga N. Kasthurirathne, a research scientist at Regenstrief Institute, and study author said.

“This algorithm is unique because it provides actionable information to clinicians, helping them to identify which patients may be more at risk for adverse events from depression,” he added.

The researchers used the new model to train random forest decision models that can predict if there’s a need for advanced care among the overall patient population and those at higher risk of depression-related adverse events.

It’s important to consider making models that can fit different patient populations. This way, the health care provider has the option to choose the best screening approach he or she needs.

“We demonstrated the ability to predict the need for advanced care for depression across various patient populations with considerable predictive performance. These efforts can easily be integrated into existing hospital workflows,” the investigators wrote in the paper.

Identifying patients in need of advanced care is important

With the high number of people who have depression, one of the most important things to do is determine who are at a higher risk of potential adverse effects, including suicide.

Depression has different types, depending on the level of risk involved. For instance, people with mild depression forms may not need assistance and can recover faster. On the other hand, those who have severe depression may require advanced care aside from what primary care providers can offer.

They may need to undergo treatment such as medications and therapies to improve their condition. Hence, the new method can act like a preventive measure to reduce the incidence of adverse events related to the condition such as suicide.

More importantly, training health care teams to successfully identify patients with severe depression can help resolve the problem. With the proper application of the novel technique, many people with depression can be treated accordingly, reducing serious complications.

Depression signs and symptoms

Health care providers need to properly identify patients with depression. The common signs and symptoms of depression include feelings of hopelessness and helplessness, loss of interest in daily activities, sleep changes, irritability, anger, appetite changes, weight changes, self-loathing, loss of energy, problems in concentrating, reckless behavior, memory problems, and unexplained pains and aches.


Journal reference:

Suranga N Kasthurirathne, Paul G Biondich, Shaun J Grannis, Saptarshi Purkayastha, Joshua R Vest, Josette F Jones. (2019). Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach. Journal of Medical Internet Research. https://www.jmir.org/2019/7/e13809/


via AI helps identify patients in need of advanced care for depression

, , , , , , , , ,

Leave a comment

[ARTICLE] Εvaluation of machine learning methods for seizure prediction in epilepsy – Full Text PDF

Abstract:

Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation.
We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.

1 Introduction

Affecting about 1 % of the world population, epilepsy is one of the most common neurological diseases. Although seizures cover relatively short periods in a patient’s life, the uncertainty when the next seizure will occur can produce a high level of anxiety [4]. For 70 % of the patients, medication can reduce the frequency of seizures or even abolish them. However, patients report that unwanted side effects of the medication as well as the unpredictability of seizures are the severest handicaps of this disease [13]. A mobile system with the ability to predict seizures can help to relief the patients’ anxiety related to the uncertainty of events by enabling them to seek shelter, apply a short acting drug or inform the treating physician about the event. The device might also be used to prevent or mitigate the seizure [12].

Usually, seizure prediction is treated as a binary classification problem of brain activity, recorded as intracranial electroencephalography (icEEG) [8], with the state of impending seizures (preictal) being labeled as 1 and periods with a big temporal distance to the next seizure (interictal) labeled as 0. In this contribution, we present a new database that has been recorded in our working group. By intensifying the cooperation of clinical research and data analysis we minimize loss of descriptive metadata. For feature extraction and classification of the recorded icEEG signals we employed both, a recently proposed deep convolutional neural network and a featurebased method.

[…]

Full Texy PGF

, , , ,

Leave a comment

[WEB PAGE] Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs – Video

"Moving an arm can involve more than 50 different muscles," UA professor Andrew Fuglevand said. "Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging."

“Moving an arm can involve more than 50 different muscles,” UA professor Andrew Fuglevand said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

UA professor Andrew Fuglevand is using artificial intelligence to stimulate multiple muscles to elicit natural movement in ways previous methods have been unable to do.
Dec. 20, 2018
Andrew Fuglevand

Andrew Fuglevand

Scientists now know that the brain controls movement in people by signaling groups of neurons to tell the muscles when and where to move. Researchers also have learned it takes a complex orchestration of many signals to produce even seemingly simple body movements.

If any of these signals are blocked or broken, such as from a spinal cord injury or stroke, the messages from the brain to the muscles are unable to connect, causing paralysis. The person’s muscles are functional, but they no longer are being sent instructions.

Andrew Fuglevand, professor of physiology at the University of Arizona College of Medicine – Tucson and professor of neuroscience at the UA College of Science, has received a $1.2 million grant from the National Institutes of Health to study electrical stimulation of the muscles as a way to restore limb movements in paralyzed individuals. Fuglevand’s goal is to restore voluntary movement to a person’s own limbs rather than relying on external mechanical or robotic devices.

Producing a wide range of movements in paralyzed limbs has been unsuccessful so far because of the substantial challenges associated with identifying the patterns of muscle stimulation needed to elicit specified movements, Fuglevand explained.

“Moving a finger involves as many as 20 different muscles at a time. Moving an arm can involve more than 50 different muscles. They all work together in an intricate ‘dance’ to produce beautifully smooth movements,” he said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

Recent advances in “machine learning,” or artificial intelligence, are making the impossible possible.

Fuglevand, who also is an affiliate professor of biomedical engineering and teaches neuroscience courses at the UA, is employing machine learning to mimic and replicate the patterns of brain activity that control groups of muscles. Tiny electrodes implanted in the muscles replay the artificially generated signals to produce complex movements.

“If successful, this approach would greatly expand the repertoire of motor behaviors available to paralyzed individuals,” he said.

“More than 5 million Americans are living with some form of paralysis, and the leading causes are stroke and spinal injury,” said Nicholas Delamere, head of the UA Department of Physiology. “New innovations in artificial intelligence, developed by scientists like Fuglevand and his team, are allowing them to decode subtle brain signals and make brain-machine interfaces that ultimately will help people move their limbs again.”

“The headway researchers have made in our understanding of artificial intelligence, machine learning and the brain is incredible,” said UA President Robert C. Robbins. “The opportunity to incorporate AI to brain-limb communication has life-changing potential, and while there are many challenges to optimize these interventions, we are really committed to making this step forward. I am incredibly excited to track Dr. Fuglevand’s progress with this new grant.”

Research reported in this release was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke, under grant No. 1R01NS102259-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
A version of this article originally appeared on the UA Health Sciences website:https://opa.uahs.arizona.edu/newsroom/news/2018/reconnecting-disconnected-ua-physiology-professor-receives-12m-nih-grant-use-ai

 

via Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs | UANews

, , , , , , , ,

Leave a comment

[WEB SITE] Novel machine learning technique for simulating the every day task of dressing

Summary: Computer scientists have devised a novel computational method, driven by machine learning techniques, to successfully and realistically simulate the multi-step process of putting on clothes.

Putting on clothes is a daily, mundane task that most of us perform with little or no thought. We may never take into consideration the multiple steps and physical motions involved when we’re getting dressed in the mornings. But that is precisely what needs to be explored when attempting to capture the motion of dressing and simulating cloth for computer animation.

Computer scientists from the Georgia Institute of Technology and Google Brain, Google’s artificial intelligence research arm, have devised a novel computational method, driven by machine learning techniques, to successfully and realistically simulate the multi-step process of putting on clothes. When dissected, the task of dressing is quite complex, and involves several different physical interactions between the character and his or her clothing, primarily guided by the person’s sense of touch.

Creating animation of a character putting on clothing is challenging due to the complex interactions between the character and the simulated garment. Most work in highly constrained character animation deals with static environments which don’t react very much to the motion of the character, notes the researchers. In contrast, clothing can respond immediately and drastically to small changes in the position of the body; clothing has the tendency to fold, stick and cling to the body, making haptic, or touch sensation, essential to the task.

Another unique challenge about dressing is that it requires the character to perform a prolonged sequence of motion involving a diverse set of subtasks, such as grasping the front layer of a shirt, tucking a hand into the shirt opening and pushing a hand through a sleeve.

“Dressing seems easy to many of us because we practice it every single day. In reality, the dynamics of cloth make it very challenging to learn how to dress from scratch,” says Alexander Clegg, lead author of the research and a computer science PhD student at the Georgia Institute of Technology. “We leverage simulation to teach a neural network to accomplish these complex tasks by breaking the task down into smaller pieces with well-defined goals, allowing the character to try the task thousands of times and providing reward or penalty signals when the character tries beneficial or detrimental changes to its policy.”

The researchers’ method then updates the neural network one step at a time to make the discovered positive changes more likely to occur in the future. “In this way, we teach the character how to succeed at the task,” notes Clegg.

Clegg and his collaborators at Georgia Tech include computer scientists Wenhao Yu, Greg Turk and Karen Liu. Together with Google Brain researcher Jie Tan, the group will present their work at SIGGRAPH Asia 2018 in Tokyo 4 December to 7 December. The annual conference features the most respected technical and creative members in the field of computer graphics and interactive techniques, and showcases leading edge research in science, art, gaming and animation, among other sectors.

In this study, the researchers demonstrated their approach on several dressing tasks: putting on a t-shirt, throwing on a jacket and robot-assisted dressing of a sleeve. With the trained neural network, they were able to achieve complex reenactment of a variety of ways an animated character puts on clothes. Key is incorporating the sense of touch into their framework to overcome the challenges in cloth simulation. The researchers found that careful selection of the cloth observations and the reward functions in their trained network are crucial to the framework’s success. As a result, this novel approach not only enables single dressing sequences but a character controller that can successfully dress under various conditions.

“We’ve opened the door to a new way of animating multi-step interaction tasks in complex environments using reinforcement learning,” says Clegg. “There is still plenty of work to be done continuing down this path, allowing simulation to provide experience and practice for task training in a virtual world.” In expanding this work, the team is currently collaborating with other researchers in Georgia Tech’s Healthcare Robotics lab to investigate the application of robotics for dressing assistance.

 

Story Source:

Materials provided by Association for Computing MachineryNote: Content may be edited for style and length.

 

via Novel machine learning technique for simulating the every day task of dressing — ScienceDaily

, , , , ,

Leave a comment

[ARTICLE] Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training – Full Text

A conceptual representation of the wrist-worn sensor system for home-based upper-limb rehabilitation. The system consists of two wearable sensors, a tablet computer to be… View more

Abstract:

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a c -statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an F -score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

Introduction

Stroke is a leading cause of severe long-term disability. In the US alone, nearly 800,000 people suffer a stroke each year [1]. The number of individuals who suffer a stroke each year is expected to rise in the coming years because the prevalence of stroke increases with age and the world population is aging [2]. Approximately 85% of individuals who have a stroke survive, but they often experience significant motor impairments. Upper-limb paresis is the most common impairment following a stroke. It affects 75% of stroke survivors and leads to limitations in the performance of Activities of Daily Living (ADL) [4].

Inability to use the stroke-affected upper limb for ADL often leads to a phenomenon that is referred to as learned non-use [5]. As patients rely more and more on the unaffected (or less impaired) upper limb [5] they progressively lose motor abilities of the stroke-affected upper limb that they may have recovered as a result of a rehabilitation intervention [6].

A high dosage of motor practice using the stroke-affected upper limb during the performance of ADL, despite considerable difficulty, stimulates neuroplasticity and motor function recovery [7]–[8][9]. Thus, it is clinically important to encourage stroke survivors to continue making appropriate use of the affected upper limb [10]–[11][12][13], in addition to engaging in rehabilitation exercises that focus on range-of-motion and functional abilities [14]–[15][16].

The use of wearable sensors has recently emerged as an efficient way to monitor the amount of upper-limb use after a stroke [17]–[18][19][20][21][22]. However, despite growing evidence of the clinical potential of these devices [23], their widespread clinical deployment has been hindered by technical limitations. A shortcoming of currently available wrist-worn devices is that they cannot distinguish between Goal-Directed (GD) movements (i.e., movements performed for a specific purposeful task) and non-Goal-Directed (non-GD) movements (e.g., the arm swinging during gait). Instead, these sensors focus on recording the number and/or intensity of any type of arm movements [10]. Consequently, non-GD movements are reflected as part of the measurements with equal importance as GD movements. This results in an overestimation of the amount of actual arm use [24]. Furthermore, monitoring the aggregate number of stroke-affected upper limb movements is not sufficient for the purpose of providing timely feedback to encourage the use of the affected limb during the performance of ADL. To promote the use of the stroke-affected limb, it is critical that feedback reflects the relative use of the affected upper limb compared to the contralateral one.

Wrist-worn movement sensors have also been applied to monitoring rehabilitation exercises in the home setting [25]–[26][27][28]. However, existing systems primarily focus on quantifying the dosage/intensity of the exercises (e.g., the duration of the exercises and the number of movement repetitions) and do not monitor if the quality of the performed exercise is appropriate. Ensuring good quality of movement during the performance of rehabilitation exercises is critical for maximizing functional recovery after a stroke [29]. Moreover, providing customized feedback regarding the quality of exercise movements can increase motivation, promote long-term adherence to a prescribed exercise regimen, and ultimately maximize clinical outcomes [30]. One of the reasons for limited exercise participation by stroke survivors is the lack of access to resources to support exercise including performance feedback from rehabilitation specialists [31]. There are no technical solutions that provide feedback regarding the quality of exercise performance for upper-limb rehabilitation after stroke.

We propose a system for aiding in functional recovery after a stroke that consists of two wearable sensors, one worn on the stroke-affected upper limb and the other on the contralateral upper limb [32] (Fig. 1). The proposed system can be used to provide timely feedback when ADL are performed. If the system detects that the patient consistently performs GD movements with the unaffected upper limb, and rarely uses the stroke-affected upper limb, then a visual or vibrotactile reminder can be triggered to encourage the patient to attempt GD movements with the stroke-affected limb. A benefit of this approach is that if a movement is critical (e.g., signing a check), patients can use the unaffected upper limb without receiving negative feedback as long as they have performed a sufficient number of movements with the affected upper limb throughout the day. Furthermore, the system promotes high-dosage motor practice with appropriate feedback to extend components of rehabilitation interventions into the home environment.[…]

via Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training – IEEE Journals & Magazine

, , , , , , , , , ,

Leave a comment

[WEB SITE] Gaming helps personalized therapy level up – Penn State University

UNIVERSITY PARK, Pa. — Using game features in non-game contexts, computers can learn to build personalized mental- and physical-therapy programs that enhance individual motivation, according to Penn State engineers.

“We want to understand the human and team behaviors that motivate learning to ultimately develop personalized methods of learning instead of the one-size-fits-all approach that is often taken,” said Conrad Tucker, assistant professor of engineering design and industrial engineering.

They seek to use machine learning to train computers to develop personalized mental or physical therapy regimens — for example, to overcome anxiety or recover from a shoulder injury — so many individuals can each use a tailor-made program.

“Using people to individually evaluate others is not efficient or sustainable in time or human resources and does not scale up well to large numbers of people,” said Tucker. “We need to train computers to read individual people. Gamification explores the idea that different people are motivated by different things.”

To begin creating computer models for therapy programs, the researchers tested how to most effectively make the completion of a physical task into a gamified application by incorporating game features like scoring, avatars, challenges and competition.

“We’re exploring here how gamification could be applied to health and wellness by focusing on physically interactive gamified applications,” said Christian Lopez, graduate student in industrial engineering, who helped conduct the tests using a virtual-reality game environment.

Screen from game designed to test features for gamification use in physical and mental therapy. Image: Kimberly Cartier / Penn State

In the virtual-reality tests, researchers asked participants to physically avoid obstacles as they moved through a virtual environment. The game system recorded their actual body positions using motion sensors and then mirrored their movements with an avatar in virtual reality.

Participants had to bend, crouch, raise their arms, and jump to avoid obstacles. The participant successfully avoided a virtual obstacle if no part of their avatar touched the obstacle. If they made contact, the researchers rated the severity of the mistake by how much of the avatar touched the obstacle.

In one of the application designs, participants could earn more points by moving to collect virtual coins, which sometimes made them hit an obstacle.

“As task complexity increases, participants need more motivation to achieve the same level of results,” said Lopez. “No matter how engaging a particular feature is, it needs to move the participant towards completing the objective rather than backtracking or wasting time on a tangential task. Adding more features doesn’t necessarily enhance performance.”

Tucker and Lopez created a predictive algorithm — a mathematical formula to forecast the outcome of an event — that rates the potential usefulness of a game feature. They then tested how well each game feature motivated participants when completing the virtual-reality tasks. They compared their test results to the algorithm’s predictions as a proof of concept and found that the formula correctly anticipated which game features best motivated people in the physically interactive tasks.

The researchers found that gamified applications with a scoring system, the ability to select an avatar, and in-game rewards led to significantly fewer mistakes and higher performance than those with a win-or-lose system, randomized gaming backgrounds and performance-based awards.

Sixty-eight participants tested two designs that differed only by the features used to complete the same set of tasks. Tucker and Lopez published their results in Computers in Human Behavior.

The researchers chose the tested game features from the top-ranked games in the Google Play app store, taking advantage of the features that make the games binge-worthy and re-playable, and then narrowed the selection based on available technology.

Their algorithm next ranked game features by how easily designers could implement them, the physical complexity of using the feature, and the impact of the feature on participant motivation and ability to complete the task. If a game feature is too technologically difficult to incorporate into the game, too physically complex, does not offer enough incentive for added effort or works against the end goal of the game, then the feature has low potential usefulness.

The researchers would also like to use these results to boost workplace performance and personalize virtual-reality classrooms for online education.

“Game culture has already explored and mastered the psychological aspects of games that make them engaging and motivating,” said Tucker. “We want to leverage that knowledge towards the goal of individualized optimization of workplace performance.”

To do this, Tucker and Lopez next want to connect performance with mental state during these gamified physical tasks. Heart rate, electroencephalogram signals and facial expressions will be used as proxies for mood and mental state while completing tasks to connect mood with game features that affect motivation.

The National Science Foundation funded this research.

Source: Gaming helps personalized therapy level up | Penn State University

, , ,

Leave a comment

[Abstract] Gait Biomechanics in the Era of Data Science – Journal of Biomechanics

Abstract

Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.

, , ,

Leave a comment

[ARTICLE] A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study – Full Text

Abstract

Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment.

In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA.

Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

Full Text PDF

 

, , , , , , , ,

Leave a comment

%d bloggers like this: