Evidence map›Paper›PMID 36365969›Full record

ArticleSensors (Basel, Switzerland)2022

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller.

Hajar Sharif, Ahmadreza Eslaminia, Pramod Chembrammel, Thenkurussi Kesavadas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hajar SharifDepartment of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.ORCID 0000-0001-6171-0143
Ahmadreza EslaminiaDepartment of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.ORCID 0000-0002-9538-3328
Pramod ChembrammelDepartment of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
Thenkurussi KesavadasDepartment of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.

Funding

National Science Foundation 1502339
6 · The paper itself

Abstract

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

Indexed as

Activities of Daily LivingStrokeAdultHandHand StrengthHumansMotionactivities of daily livinghand grasps classificationLeap Motion Controller

Identifiers

PMID36365969
PMCPMC9656805

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.