scholarly journals Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 199
Author(s):  
Ciro Mennella ◽  
Susanna Alloisio ◽  
Antonio Novellino ◽  
Federica Viti
Keyword(s):  
Systematic Review ◽  
Functional Assessment ◽  
Inertial Measurement Unit ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Measurement Unit ◽  
Technology Evaluation ◽  
Inertial Measurement ◽  
Future Studies ◽  
Sensing Technology

Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.

scholarly journals Systematic review on the method and type of convergence study of Inertial Measurement Unit

2020 ◽  
Vol 16 (S10) ◽  
Author(s):  
Hey Sig Lee ◽  
Ga‐In Shin ◽  
Ye‐Shin Woo ◽  
Da‐Sol Park ◽  
Hae Yean Park
Keyword(s):  
Systematic Review ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Convergence Study

scholarly journals Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2511
Author(s):  
Filipe Manuel Clemente ◽  
Zeki Akyildiz ◽  
José Pino-Ortega ◽  
Markel Rico-González
Keyword(s):  
Systematic Review ◽  
Inertial Measurement Unit ◽  
Great Majority ◽  
Measurement Unit ◽  
Cochrane Library ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Monitoring Process ◽  
Load Monitoring ◽  
Meta Analyses

The use of inertial measurement unit (IMU) has become popular in sports assessment. In the case of velocity-based training (VBT), there is a need to measure barbell velocity in each repetition. The use of IMUs may make the monitoring process easier; however, its validity and reliability should be established. Thus, this systematic review aimed to (1) identify and summarize studies that have examined the validity of wearable wireless IMUs for measuring barbell velocity and (2) identify and summarize studies that have examined the reliability of IMUs for measuring barbell velocity. A systematic review of Cochrane Library, EBSCO, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 161 studies initially identified, 22 were fully reviewed, and their outcome measures were extracted and analyzed. Among the eight different IMU models, seven can be considered valid and reliable for measuring barbell velocity. The great majority of IMUs used for measuring barbell velocity in linear trajectories are valid and reliable, and thus can be used by coaches for external load monitoring.

scholarly journals Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ze-Jian Chen ◽  
Chang He ◽  
Ming-Hui Gu ◽  
Jiang Xu ◽  
Xiao-Lin Huang
Keyword(s):  
Upper Extremity ◽  
Motor Function ◽  
Upper Limb ◽  
Inertial Measurement Unit ◽  
Clinical Decision Making ◽  
Motor Impairment ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Subacute Stroke ◽  
Kinematic Evaluation

Kinematic evaluation via portable sensor system has been increasingly applied in neurological sciences and clinical practice. However, conventional kinematic evaluation rarely extends the context beyond the motor impairment level. In addition, kinematic tasks with numerous items could be complex and time consuming that pose a burden to test applications and data processing. The study aimed to explore the correlation of finger-to-nose task (FNT) kinematics via Inertial Measurement Unit with upper limb motor function in subacute stroke. In this study, six FNT kinematic variables were used to measure movement time, smoothness, and velocity in 37 participants with subacute stroke. Upper limb motor function was evaluated with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and modified Barthel Index (MBI). As a result, mean velocity, peak velocity, and the number of movement units were associated with the clinical assessments. The multivariable linear regression models could estimate 55%, 51%, and 32% of variance in FMA-UE, ARAT, and MBI, respectively. In addition, age, gender, type of stroke, and paretic side had no significant effects on these associations. Results show that FNT kinematic variables measured via Inertial Measurement Unit are associated with upper extremity motor function in individuals with subacute stroke. The objective kinematic evaluation may be suitable for predicting clinical measures of motor impairment and capacity to understand upper extremity motor recovery and clinical decision making after stroke. This trial is registered with ChiCTR1900026656.

scholarly journals Validity and reliability of the inertial measurement unit for height jump estimation: A systematic review

2020 ◽  
Author(s):  
Filipe Manuel Clemente ◽  
Rui Silva ◽  
Zeki Akyildiz ◽  
José Pino-Ortega ◽  
Markel Rico-González
Keyword(s):  
Systematic Review ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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