Smartwatches Able to Detect Very Early Signs of Parkinson's

With your risk of Parkinsons post stroke does your doctor have enough functioning brain cells to  use this on you?

Parkinson’s Disease May Have Link to Stroke March 2017 

Do you prefer your doctor incompetence in this NOT KNOWING? OR NOT DOING?

 

The latest here:

Smartwatches Able to Detect Very Early Signs of Parkinson's

Changes in movement detected passively by smartwatches can help flag Parkinson's disease (PD) years before symptom onset, new research shows.

An analysis of wearable motion-tracking data from UK Biobank participants showed a strong correlation between reduced daytime movement over 1 week and a clinical diagnosis of PD up to 7 years later.

"Smartwatch data is easily accessible and low-cost. By using this type of data, we would potentially be able to identify individuals in the very early stages of Parkinson's disease within the general population," lead researcher Cynthia Sandor, PhD, from Cardiff University, said in a statement.

"We have shown here that a single week of data captured can predict events up to seven years in the future. With these results we could develop a valuable screening tool to aid in the early detection of Parkinson's," she added.

"This has implications both for research, in improving recruitment into clinical trials, and in clinical practice, in allowing patients to access treatments at an earlier stage, in future when such treatments become available," said Sandor.

The study was published online July 3 in Nature Medicine.

Novel Biomarker for PD

Using machine learning, the researchers analyzed accelerometry data from 103,712 UK Biobank participants who wore a medical-grade smartwatch for a 7-day period in 2013 to 2016.

At the time of or within 2 years after accelerometry data collection, 273 participants were diagnosed with PD. An additional 196 individuals received a new PD diagnosis more than 2 years after accelerometry data collection (the prodromal group).

The patients with prodromal symptoms of PD and those who were diagnosed with PD showed a significantly reduced daytime acceleration profile up to 7 years before diagnosis, compared with age- and sex-matched healthy control persons, the researchers found.

The reduction in acceleration both before and following diagnosis was unique to patients with PD, "suggesting this measure to be disease specific with potential for use in early identification of individuals likely to be diagnosed with PD," they write.

Accelerometry data proved more accurate than other risk factors (lifestyle, genetics, blood chemistry) or recognized prodromal symptoms of PD in predicting whether an individual would develop PD.

"Our results suggest that accelerometry collected with wearable devices in the general population could be used to identify those at elevated risk for PD on an unprecedented scale and, importantly, individuals who will likely convert within the next few years can be included in studies for neuroprotective treatments," the researchers conclude in their article.

High-Quality Research

Weighing in on the results in a statement from the UK-based nonprofit Science Media Centre, José López Barneo, MD, PhD, with the University of Seville, Spain, said this "good quality" study "fits well with current knowledge."

Barneo noted that other investigators have also observed that slowness of movement is a characteristic feature of some people who subsequently develop PD.

But these studies involved preselected cohorts of persons at risk of developing PD, or they were carried out in a hospital that required healthcare staff to conduct the movement analysis. In contrast, the current study was conducted in a very large cohort from the general UK population.

Also weighing in, José Luis Lanciego, MD, PhD, with the University of Navarra, Spain, said the "main value of this study is that it has demonstrated that accelerometry measurements obtained using wearable devices (such as a smartwatch or other similar devices) are more useful than the assessment of any other potentially prodromal symptom in identifying which people in the [general] population are at increased risk of developing Parkinson's disease in the future, as well as being able to estimate how many years it will take to start suffering from this neurodegenerative process.

"In these diseases, early diagnosis is to some extent questionable, as early diagnosis is of little use if neuroprotective treatment is not available," Lanciego noted.

"However, it is of great importance for use in clinical trials aimed at evaluating the efficacy of new potentially neuroprotective treatments whose main objective is to slow down ― and, ideally, even halt ― the clinical progression that typically characterizes Parkinson's disease," Lanciego added.

The study was funded by the UK Dementia Research Institute, the Welsh Government and Cardiff University. Sandor, Barneo and Lanciego have no relevant disclosures.

Nature Med. Published online July 3, 2023. Abstract

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

By not writing this up as a protocol this is totally fucking useless for any survivor's recovery.  God, I absolutely hate the fact that mentors and senior researchers are not telling their underlings that the only goal in stroke is 100% recovery and ALL

RESEARCH should lead to that. And that won't occur until survivors are in charge.

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

• Jessica Barth,
• Joeseph W. Klaesner &
• Catherine E. Lang 

Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 138 (2020) Cite this article

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Abstract

Background

Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke.

Methods

This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods.

Results

Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score.

Conclusions

Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

Introduction

As advances in medicine persist, more people are surviving a stroke. Over 80% of those affected will have persistent hemiparesis of their upper limb [1]. These people will be left with chronic disability when trying to complete their activities of daily living (ADL), and an even larger number will not resume their normal daily activities completed prior to stroke [2]. At this time, physical and occupational therapy is the only option available to improve upper limb use after stroke. The ultimate goal of these therapies is to restore the use of the upper limb to the same level it was used before the stroke. Most individuals, however, only partially regain function of their upper limb requiring compensations of the upper limb to complete daily tasks. The differentiation between restoration of upper limb movement and compensation is an area of high interest in stroke rehabilitation [3]. Compensation can occur on multiple levels, such as using an alternative movement pattern, using an alternative tool or support (e.g. built up spoon for self-feeding), and/or using an alternate means to achieve the task (e.g. completion of an activity by a spouse rather than the individual). For the purposes of this paper, compensatory movements will refer to completion of the same movement but with an alternative movement pattern. Specifically, this level of compensatory movements typically describe accessory movements of the head, trunk and upper limb that an individual incorporates in order to accomplish tasks. A simple example is that if an individual lacks shoulder flexion, or the ability to raise their arm in front of them, the individual lifts their arm by raising it more to the side and bending forward with the trunk [4, 5]. Many in the neurorehabilitation field view compensation and restoration as a dichotomy, where individuals will either be classified as using compensatory movement patterns or restored movement patterns. Return of upper limb function may be better conceptualized as a gradient, with individuals having degrees of compensatory movement patterns [6].

Currently, many in-clinic standardized assessments have some aspects that measure use of compensatory movement patterns. For example, the Reaching Performance Scale specifically assesses compensatory movements of the upper limb during reaching in people with hemiparesis [7]. The Wolf Motor Function Test’s Functional Ability Scale reduces scores if movement compensations were observed during item completion [8]. The Fugl-Meyer arm motor scale, an impairment scale, focused on movement patterns, takes points off where specific compensatory movements are observed on each item [9]. Additionally the Action Research Arm Test (ARAT) scores individuals completing functional reach to grasp tasks with consideration of the quality of the reach and grasp pattern along with the fluidity or precision of the task [10, 11]. Standardized assessments have the ability to measure upper limb functional capacity and compensatory movements of the upper limb after stroke, however these assessments only capture one piece of upper limb recovery after stroke.

The current gold standard in the field to measure quality of movement or compensatory movements is through the use of 3D kinematics [12]. Kinematics provides the most detailed assessment of how an individual moves after stroke. It is not realistic, however, to use kinematics in the clinic for all patients due to cost of equipment, time required to test, and training of personnel. This leaves standardized assessments to be the alternative and most accessible measure of compensatory movement patterns. This gap in measurement has lead our lab to question how we might utilize our existing accelerometry methodology to capture some of these changes in compensatory movement.

In-clinic assessments are limited in that they measure the individual’s ability to use the limb in a standardized, structured setting, leaving the individuals actual activity of the limb during daily life unaccounted for. Over the past 5 years, methodology has been developed to measure upper limb activity in daily life using wearable sensors (accelerometers) [13, 14]. Accelerometry can quantify how much and how often a person uses their affected limb during their daily life, bridging the gap between in and out-of-clinic assessment. Current accelerometer metrics quantify time, magnitude and variability of movement of the upper limb [15,16,17,18,19]. A limitation of current accelerometry methods is that they quantify the amount of movement, but do not capture the quality of a person’s movement, an important concept to assess compensation versus restoration.

The purpose of this secondary analysis was to characterize how accelerometer variables reflect upper limb compensatory movement patterns after stroke. Relationships between compensatory movement patterns and accelerometer variables were calculated for both in-clinic and out-of-clinic time points. Both time points were included as the in-clinic time includes completion of standardized assessments and participation in an intensive upper limb therapy protocol. Due to the nature of the therapy protocol, we anticipated there may be different relationships because during the in-clinic time participants are intentionally training their affected limb. The out-of-clinic recordings captures the individual in their free-living environment, providing a more realistic picture of how the individual uses their upper limb in daily life. It is hypothesized that quantitative metrics from accelerometers both in and out-of-clinic will have moderate associations with compensatory movement patterns of the upper limb.

 

Poincaré Descriptors for Identifying Hemiparesis in Acute Stroke using Wearable Accelerometry

 Have your doctor explain Poincaré and how this is going to get you 100% recovered.

Poincaré map - Wikipedia

In mathematics, particularly in dynamical systems, a first recurrence map or Poincaré map, named after Henri Poincaré, is the intersection of a periodic orbit in the state space of a continuous dynamical system with a certain lower-dimensional subspace, called the Poincaré section, transversal to the flow of the system .

Poincaré Descriptors for Identifying Hemiparesis in Acute Stroke using Wearable Accelerometry

4

Author(s)

Shreyasi Datta ; handan K. Karmakar ; Bernard Yan ; Marimuthu Palaniswami

All Authors

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Abstract

Document Sections

• I.
  INTRODUCTION
• II.
  DATA AND METHODS
• III.
  RESULTS AND DISCUSSION
• IV.
  CONCLUSION

Authors

Figures

References

Keywords

Footnotes

Abstract:

Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, that severely affects upper limb movements. Continuous monitoring of the progression of hemiparesis requires manual observation of the limb movements at regular intervals and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparetic severity in acute stroke patients through bivariate Poincaré analysis between accelerometer data from the two hands during spontaneous and instructed movements. Experiments show that while the bivariate Poincaré descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, a novel descriptor called Complex Cross-Correlation Measure (C3M) can distinguish between moderate and severe hemiparesis. Further, we justify the use of C3M by showing that it is described by multiple-lag cross-correlations, representing the co-ordination of activity between two hands. The descriptors are compared against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for evaluation of hemiparetic severity, and studied using statistical tests for developing supervised models for hemiparesis classification.Clinical relevance—This study establishes the suitability of wrist-worn accelerometers in identifying hemiparetic severity in stroke patients through novel descriptors of hand co-ordination.

 

Relationship Between Clinical Measures of Upper Limb Movement Quality and Activity Poststroke

You mean objective measurement of movement is not available from any of these? Must stroke survivors take charge and explain how to do stroke research since you are not keeping up-to-date in your chosen field?  And neither are your mentors and senior researchers. Firings must take place.

• accelerometers (24 posts to June 2013)
• accelerometry (4 post to February 2012)
• Inertial Motion Capture Systems (2) 
• motion analysis (4 post to April 2013)
• motion captu/re (7 post to March 2013)
• motion capture software (1 post to July 2017)
• motion capture system (3 post to June 2013)
• Motion Pattern Tracking (1 to January 2016)
• motion sensors (12 posts to August 2012)
• motion tracking (2 post to December 2015)
• motion-capture sensors (1 post to May 2017)

 

Relationship Between Clinical Measures of Upper Limb Movement Quality and Activity Poststroke

Show all authors

Mindy F. Levin, PhD*, Vimonwan Hiengkaew, Yongchai Nilanont, MD, ...

First Published May 10, 2019 Research Article

https://doi.org/10.1177/1545968319847969

Article information

Abstract

Background. Understanding the relationship between movement quality (impairment) and performance (activity) in poststroke patients is important for rehabilitation intervention studies. This has led to an interest in kinematic characterization of upper limb motor impairment. Since instrumented motion analysis is not readily clinically available, observational kinematics may be a viable alternative.  
Objective. To determine if upper limb movement quality during a reach-to-grasp task identified by observation could be used to describe the relationship between motor impairments and the time to perform functional tasks.  
Methods. Cross-sectional, secondary analysis of baseline data from 141 participants with stroke, age 18 to 85 years, who participated in a multicenter randomized controlled trial. Clinical assessment of movement quality using the Reaching Performance Scale for Stroke (RPSS–Close and Far targets) and of performance (activity) from the Wolf Motor Function Test (WMFT–7 items) was assessed. The degree to which RPSS component scores explained scores on WMFT items was determined by multivariable regression.  
Results. Clinically significant decreases (>2 seconds) in performance time for some of the more complex WMFT tasks involving prehension were predicted from RPSS–Close and Far target components. Trunk compensatory movements did not predict either increases or decreases in performance time for the WMFT tasks evaluated. Overall, the strength of the regression models was low.  
Conclusions. In lieu of kinematic analysis, observational clinical movement analysis may be a valid and accessible method to determine relationships between motor impairment, compensations and upper limb function in poststroke patients. Specific relationships are unlikely to generalize to all tasks due to kinematic redundancy and task specificity.

Keywords stroke, motor control, upper limb, impairment, activity, redundancy

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A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy

By applying this to survivors you could determine how much of our fatigue is based upon our muscle use. Then our doctors might have some clue for the research direction to go down to solve our fatigue problems.  Because right now we have absolutely nothing  to help us with our fatigue.
http://www.jneuroengrehab.com/content/11/1/116/abstract

Jennifer M Ryan, Michael Walsh and John Gormley

Author Affiliations

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Journal of NeuroEngineering and Rehabilitation 2014, 11:116  doi:10.1186/1743-0003-11-116
Published: 5 August 2014

Abstract (provisional)

Background

Advanced accelerometry-based devices have the potential to improve the measurement of everyday energy expenditure (EE) in people with cerebral palsy (CP). The aim of this study was to investigate the ability of two such devices (the Sensewear ProArmband and the Intelligent Device for Energy Expenditure and Activity) and the ability of a traditional accelerometer (the RT3) to estimate EE in adults and children with CP.

Methods

Adults (n = 18; age 31.9 +/- 9.5 yr) and children (n = 18; age 11.4 +/- 3.2 yr) with CP (GMFCS levels I-III) participated in this study. Oxygen uptake, measured by the Oxycon Mobile portable indirect calorimeter, was converted into EE using Weir's equation and used as the criterion measure. Participants' EE was measured simultaneously with the indirect calorimeter and three accelerometers while they rested for 10 minutes in a supine position, walked overground at a maximal effort for 6 minutes, and completed four treadmill activities for 5 minutes each at speeds of 1.0 km.h-1, 1.0 km.h-1 at 5% incline, 2.0 km.h-1, and 4.0 km.h-1.

Results

In adults the mean absolute percentage error was smallest for the IDEEA, ranging from 8.4% to 24.5% for individual activities (mean 16.3%). In children the mean absolute percentage error was smallest for the SWA, ranging from 0.9% to 23.0% for individual activities mean (12.4%). Limits of agreement revealed that the RT3 provided the best agreement with the indirect calorimeter for adults and children. The upper and lower limits of agreement for adults were 3.18 kcal.min-1 (95% CI = 2.66 to 3.70 kcal.min-1) and -2.47 kcal.min-1 (95% CI = -1.95 to -3.00 kcal.min-1), respectively. For children, the upper and lower limits of agreement were 1.91 kcal.min-1 (1.64 to 2.19 kcal.min-1) and -0.92 kcal.min-1 (95% CI = -1.20 to -0.64 kcal.min-1) respectively. These limits of agreement represent -67.2% to 86.3% of mean EE for adults and -36.5% to 76.3% of mean EE for children.

Conclusions

Although the RT3 provided the best agreement with the indirect calorimeter the RT3 could significantly overestimate or underestimate individual estimates of EE. The development of CP-specific algorithms may improve the ability of these devices to estimate EE in this population.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

Low-Cost Wearable Data Acquisition for Stroke Rehabilitation: A Proof-of-Concept Study on Accelerometry for Functional Task Assessment

This just makes too much sense, objective evaluations of movements. It will take 30+ years to make it to the survivors.

Low-Cost Wearable Data Acquisition for Stroke Rehabilitation: A Proof-of-Concept Study on Accelerometry for Functional Task Assessment

Authors

Antonio J. Salazar, MSc^{1, 2}, Ana S. Silva, MSc^{1, 2}, Claudia Silva, MSc³, Carla M. Borges, Eng², Miguel V. Correia, PhD^{1, 2, 4}, Rubim S. Santos, PhD³, Joao P. Vilas-Boas, PhD⁴

¹INESC Technology and Science (INESC TEC), Porto, Portugal
²Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
³Centro de Estudos do Movimento e Actividade Humana (CEMAH), ESTSP-IPP, Vila Nova de Gaia, Portugal
⁴Biomechanics Laboratory (LABIOMEP), Faculdade de Desporto, Universidade do Porto, Porto, Portugal

Abstract

Background: An increasingly aging society and consequently rising number of patients with poststroke-related neurological dysfunctions are forcing the rehabilitation field to adapt to ever-growing demands. Although clinical reasoning within rehabilitation is dependent on patient movement performance analysis, current strategies for monitoring rehabilitation progress are based on subjective time-consuming assessment scales, not often applied. Therefore, a need exists for efficient nonsubjective monitoring methods. Wearable monitoring devices are rapidly becoming a recognized option in rehabilitation for quantitative measures. Developments in sensors, embedded technology, and smart textile are driving rehabilitation to adopt an objective, seamless, efficient, and cost-effective delivery system. This study aims to assist physiotherapists’ clinical reasoning process through the incorporation of accelerometers as part of an electronic data acquisition system. Methods: A simple, low-cost, wearable device for poststroke rehabilitation progress monitoring was developed based on commercially available inertial sensors. Accelerometry data acquisition was performed for 4 first-time poststroke patients during a reach-press-return task. Results: Preliminary studies revealed acceleration profiles of stroke patients through which it is possible to quantitatively assess the functional movement, identify compensatory strategies, and help define proper movement. Conclusion: An inertial data acquisition system was designed and developed as a low-cost option for monitoring rehabilitation. The device seeks to ease the data-gathering process by physiotherapists to complement current practices with accelerometry profiles and aid the development of quantifiable methodologies and protocols.

The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study

Prevent your falls, your therapist will be able to translate this into a stroke protocol.
http://www.jneuroengrehab.com/content/10/1/7/abstract

Abstract (provisional)

Background

Gait variables derived from trunk accelerometry may predict the risk of falls; however, their associations with falls are not fully understood. The purpose of the study was to determine which gait variables derived from upper and lower trunk accelerometry are associated with the incidence of falls, and to compare the discriminative ability of gait variables and physical performance.

Methods

This study was a 1-year prospective study. Older people (n = 73) walked normally while wearing accelerometers attached to the upper and lower trunk. Participants were classified as fallers (n = 16) or non-fallers (n = 57) based on the incidence of falls over 1 year. The harmonic ratio (HR) of the upper and lower trunk was measured. Physical performance was measured in five chair stands and in the timed up and go test.

Results

The HR of the upper and lower trunk were consistently lower in fallers than non-fallers (P < 0.05). Upper trunk HR, was independently associated with the incidence of falls (P < 0.05) after adjusting for confounding factors including physical performances. Consequently, upper trunk HR showed high discrimination for the risk of falls (AUC = 0.81).

Conclusions

HR derived from upper trunk accelerometry may predict the risk of falls, independently of physical performance. The discriminative ability of HR for the risk of falls may have some validity, and further studies are needed to confirm the clinical relevance of trunk HR.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

COMPENSATORY MOVEMENT DETECTION THROUGH INERTIAL SENSOR POSITIONING FOR POST-STROKE REHABILITATION

Another idea on measuring what is going wrong with movements.
http://paginas.fe.up.pt/~dee08011/files/Download/BIOSIGNALS2012.pdf
Keywords: Rehabilitation, stroke patients, compensatory movements, sensor positioning, accelerometry
Abstract: An increasing ageing society and consequently rising number of post-stroke related neurological dysfunction patients are forcing the rehabilitation field to adapt to ever-growing demands. In parallel, an unprecedented number of research efforts and technological solutions meant for human monitoring are continuously influencing traditional methodologies, causing paradigm shifts; extending the therapist patient dynamics. Compensatory movements can be observed in post-stroke patient when performing functional tasks. Although some controversy remains regarding the functional benefits of compensatory movement as a way of accomplish a given task, even in the presence of a motor deficit; studies suggest that such maladaptive strategies may limit the plasticity of the nervous system to enhance neuro-motor recovery. This preliminary study intends to aid in the development of a system for compensatory movement detection in stroke patients through the use of accelerometry data. A post-stroke patients group is presented and discussed, instructed to perform reach and press movements while sensors were positioned at different location on the arm, forearm and trunk, in order to assess sensor positioning influence. Results suggest that P1 is advantageous for compensatory elevation movement detection at the shoulder; P4 seems the most appropriate for detecting the abduction; and P5 presents a reasonable sensitivity for detection of anteriorization and rotation of the trunk.
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