scholarly journals Sensor-Based Categorization of Upper Limb Performance in Daily Life of Persons With and Without Neurological Upper Limb Deficits

2021 ◽  
Vol 2 ◽  
Author(s):  
Jessica Barth ◽  
Keith R. Lohse ◽  
Jeffrey D. Konrad ◽  
Marghuertta D. Bland ◽  
Catherine E. Lang
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Sensor Technology ◽  
Cluster Solution ◽  
Accelerometer Data ◽  
Solution Quality ◽  
Performance Variables ◽  
Two Samples ◽  
Multiple Variables ◽  
Input Variables

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

An Empirical Analysis of the Obtrusiveness of and Participants' Compliance with the Electronically Activated Recorder (EAR)

2007 ◽  
Vol 23 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Matthias R. Mehl ◽  
Shannon E. Holleran
Keyword(s):  
Empirical Analysis ◽  
Sampling Method ◽  
Daily Life ◽  
Assessment Method ◽  
Data Confidentiality ◽  
Short Term ◽  
Naturally Occurring ◽  
Two Samples ◽  
Audio Recorder ◽  
Term Monitoring

Abstract. In this article, the authors provide an empirical analysis of the obtrusiveness of and participants' compliance with a relatively new psychological ambulatory assessment method, called the electronically activated recorder or EAR. The EAR is a modified portable audio-recorder that periodically records snippets of ambient sounds from participants' daily environments. In tracking moment-to-moment ambient sounds, the EAR yields an acoustic log of a person's day as it unfolds. As a naturalistic observation sampling method, it provides an observer's account of daily life and is optimized for the assessment of audible aspects of participants' naturally-occurring social behaviors and interactions. Measures of self-reported and behaviorally-assessed EAR obtrusiveness and compliance were analyzed in two samples. After an initial 2-h period of relative obtrusiveness, participants habituated to wearing the EAR and perceived it as fairly unobtrusive both in a short-term (2 days, N = 96) and a longer-term (10-11 days, N = 11) monitoring. Compliance with the method was high both during the short-term and longer-term monitoring. Somewhat reduced compliance was identified over the weekend; this effect appears to be specific to student populations. Important privacy and data confidentiality considerations around the EAR method are discussed.

scholarly journals Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

2020 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Mikko Kärmeniemi ◽  
Soile Puhakka ◽  
Maarit Kangas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Mining ◽  
Decision Tree ◽  
Sitting Time ◽  
Accelerometer Data ◽  
Relative Importance ◽  
Interaction Detection ◽  
Data Mining Approach ◽  
Input Variables

Abstract Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive depending on participants’ activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results: Of the 4,582 participants with valid accelerometer data at the latest follow-up, 2,701 and 1,881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds after exercise (SED: B=-16.1, LPA: B=9.9, and MVPA: B=9.6), average weekday total sitting time (SED: B=34.1, LPA: B=-25.3, and MVPA: B=-5.8), and extravagance score (SED: B=6.3 and LPA: B=-3.7). Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

Evaluating upper limb function after stroke using the free-living accelerometer data

2020 ◽  
Vol 29 (11) ◽  
pp. 3249-3264 ◽  
Author(s):  
Lin Tang ◽  
Shane Halloran ◽  
Jian Qing Shi ◽  
Yu Guan ◽  
Chunzheng Cao ◽  
...  
Keyword(s):  
Upper Limb ◽  
Daily Living ◽  
Gaussian Mixture ◽  
Activity Level ◽  
Accelerometer Data ◽  
Free Living ◽  
Limb Function ◽  
Nonlinear Mixed Effects Model ◽  
Analysis Techniques ◽  
Upper Limb Function

Accelerometer devices are becoming efficient tools in clinical studies for automatically measuring the activities of daily living. Such data provides a time series describing activity level at every second and displays a subject’s activity pattern throughout a day. However, the analysis of such data is very challenging due to the large number of observations produced each second and the variability among subjects. The purpose of this study is to develop efficient statistical analysis techniques for predicting the recovery level of the upper limb function after stroke based on the free-living accelerometer data. We propose to use a Gaussian Mixture Model (GMM)-based method for clustering and extracting new features to capture the information contained in the raw data. A nonlinear mixed effects model with Gaussian Process prior for the random effects is developed as the predictive model for evaluating the recovery level of the upper limb function. Results of applying to the accelerometer data for patients after stroke are presented.

scholarly journals Upper Limb Performance in Daily Life Improves Over the First 12 Weeks Poststroke

2019 ◽  
Vol 33 (10) ◽  
pp. 836-847 ◽  
Author(s):  
Kimberly J. Waddell ◽  
Michael J Strube ◽  
Rachel G. Tabak ◽  
Debra Haire-Joshu ◽  
Catherine E. Lang
Keyword(s):  
Psychosocial Factors ◽  
Hierarchical Linear Modeling ◽  
Upper Limb ◽  
Daily Life ◽  
Negative Relationship ◽  
Perceived Barriers ◽  
Recovery Trajectory ◽  
Moderating Role ◽  
Over Time

Background. Upper limb (UL) performance, or use, in daily life is complex and likely influenced by many factors. While the recovery trajectory of UL impairment poststroke is well documented, little is known about the recovery trajectory of sensor-measured UL performance in daily life early after stroke and the potential moderating role of psychosocial factors. Objective. To examine the recovery trajectory of UL performance within the first 12 weeks poststroke and characterize the potential moderating role of belief, confidence, and motivation on UL performance. Methods. This was a longitudinal, prospective cohort study quantifying UL performance and related psychosocial factors early after stroke. UL performance was quantified via bilateral, wrist-worn accelerometers over 5 assessment sessions for 24 hours. Belief, confidence, and motivation to use the paretic UL, and self-perceived barriers to UL recovery were quantified via survey. Change in 4 accelerometer variables and the moderating role of psychosocial factors was tested using hierarchical linear modeling. The relationship between self-perceived barriers and UL performance was tested via Spearman rank-order correlation analysis. Results. UL performance improved over the first 12 weeks after stroke. Belief, confidence, and motivation did not moderate UL performance over time. There was a negative relationship between UL performance and self-perceived barriers to UL recovery at week 2, which declined over time. Conclusions. Sensor-measured UL performance can improve early after stroke. Early after stroke, rehabilitation interventions may not need to directly target belief, confidence, and motivation but may instead focus on reducing self-perceived barriers to UL recovery.

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

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Jessica Barth ◽  
Joeseph W. Klaesner ◽  
Catherine E. Lang
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Secondary Analysis ◽  
Movement Patterns ◽  
Limb Movement ◽  
Standardized Assessments ◽  
Data Set ◽  
Compensatory Movement ◽  
Paretic Limb ◽  
Upper Limb Paresis

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.

1P1-J08 Development of a Power Assistance Apparatus for an Upper Limb Supporting the Behaviors in Daily Life

2009 ◽  
Vol 2009 (0) ◽  
pp. _1P1-J08_1-_1P1-J08_4
Author(s):  
Tatsuya AKIYAMA ◽  
Eiichirou TANAKA ◽  
Tadaaki IKEHARA ◽  
Shozo SAEGUSA ◽  
Daisuke TSUNODA
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Power Assistance

The Stroke Upper-Limb Activity Monitor: Its Sensitivity to Measure Hemiplegic Upper-Limb Activity During Daily Life

2007 ◽  
Vol 88 (9) ◽  
pp. 1121-1126 ◽  
Author(s):  
Mark de Niet ◽  
Johannes B. Bussmann ◽  
Gerard M. Ribbers ◽  
Henk J. Stam
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Activity Monitor

scholarly journals Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

2020 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Mikko Kärmeniemi ◽  
Soile Puhakka ◽  
Maarit Kangas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Mining ◽  
Decision Tree ◽  
Sitting Time ◽  
Accelerometer Data ◽  
Relative Importance ◽  
Interaction Detection ◽  
Data Mining Approach ◽  
Input Variables

Abstract Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior.Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors.Results: Of the 4,582 participants with valid accelerometer data at the latest follow-up, 2,701 and 1,881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds after exercise (SED: B=-16.1, LPA: B=9.9, and MVPA: B=9.6), average weekday total sitting time (SED: B=34.1, LPA: B=-25.3, and MVPA: B=-5.8), and extravagance score (SED: B=6.3 and LPA: B=-3.7).Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

scholarly journals Reaching higher: External scapula assistance can improve upper limb function in humans with irreversible scapula alata

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Anna-Maria Georgarakis ◽  
Michele Xiloyannis ◽  
Christian Dettmers ◽  
Michael Joebges ◽  
Peter Wolf ◽  
...  
Keyword(s):  
Upper Limb ◽  
Perceived Exertion ◽  
Daily Life ◽  
Scapular Dyskinesis ◽  
Limb Function ◽  
Surgical Interventions ◽  
Upper Limb Function ◽  
Shoulder Flexion ◽  
Arm Elevation ◽  
Shoulder Blade

Abstract Background Scapular dyskinesis, i.e., the deviant mobility or function of the scapula, hampers upper limb function in daily life. A typical sign of scapular dyskinesis is a scapula alata—a protrusion of the shoulder blade during arm elevation. While some reversible causes of scapula alata can be treated with therapy, other, irreversible causes require invasive surgical interventions. When surgery is not an option, however, severe limitations arise as standard approaches for assisting the scapula in daily life do not exist. The aim of this study was to quantify functional improvements when external, i.e., non-invasive, scapula assistance is provided. Methods The study was designed as a randomized controlled crossover trial. Eight participants with a scapula alata due to muscular dystrophy performed arm elevations in shoulder flexion and abduction while unassisted (baseline), externally assisted by a trained therapist, and externally assisted by a novel, textile-based scapula orthosis. Results With therapist assistance, average arm elevation increased by 17.3° in flexion (p < 0.001, 95% confidence interval of the mean $$C{I}_{95\%}=\hspace{0.17em}\left[9.8^\circ , 24.9^\circ \right]$$ C I 95 % = 9 . 8 ∘ , 24 . 9 ∘ ), and by 11.2° in abduction (p < 0.01, $$C{I}_{95\%}=\left[4.7^\circ , 17.7^\circ \right]$$ C I 95 % = 4 . 7 ∘ , 17 . 7 ∘ ), constituting the potential of external scapula assistance. With orthosis assistance, average arm elevation increased by 6.2° in flexion ($$C{I}_{95\%}=\left[0.4^\circ ,11.9^\circ \right]$$ C I 95 % = 0 . 4 ∘ , 11 . 9 ∘ ) and by 5.8° in abduction ($$C{I}_{95\%}=\left[3.0^\circ ,8.5^\circ \right]$$ C I 95 % = 3 . 0 ∘ , 8 . 5 ∘ ). Remarkably, in three participants, the orthosis was at least as effective as the therapist. Moreover, orthosis assistance reduced average perceived exertion by 1.25 points (Borg Scale) when elevating a filled bottle during a simulated daily living task. Conclusion These findings indicate a large potential for future advancements in orthotics. Already now, the textile-based scapula orthosis presented here is a feasible tool for leveraging the benefits of external scapula assistance when a therapist is unavailable, as encountered in daily life scenarios. Trial Registration ClincalTrials.gov (ID NCT04154098). Registered: November 6th 2019, https://clinicaltrials.gov/ct2/show/NCT04154098?term=scapula+orthosis&draw=2&rank=1 Graphic abstract

Measurement of Upper Limb Function During Daily Life After Stroke

2018 ◽  
pp. 307-311
Author(s):  
Jeremia P. O. Held ◽  
Peter H. Veltink ◽  
Fokke B. van Meulen ◽  
Andreas R. Luft ◽  
Jaap H. Buurke
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Limb Function ◽  
Upper Limb Function
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