A smart data annotation tool for multi-sensor activity recognition

2017 ◽  
Author(s):  
Alexander Diete ◽  
Timo Sztyler ◽  
Heiner Stuckenschmidt
Keyword(s):  
Activity Recognition ◽  
Annotation Tool ◽  
Data Annotation ◽  
Smart Data

scholarly journals A Semi-Automatic Annotation Approach for Human Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 501 ◽  
Author(s):  
Patrícia Bota ◽  
Joana Silva ◽  
Duarte Folgado ◽  
Hugo Gamboa
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
High Performance ◽  
Model Performance ◽  
Human Activity Recognition ◽  
Embedded Sensors ◽  
Data Annotation ◽  
Indoor Location ◽  
Annotation Process ◽  
Comprehensive Study

Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and transportation. The development of Human Activity Recognition (HAR) algorithms involves the collection of a large amount of labelled data which should be annotated by an expert. However, the data annotation process on large datasets is expensive, time consuming and difficult to obtain. The development of a HAR approach which requires low annotation effort and still maintains adequate performance is a relevant challenge. We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and the required volume of annotated data to obtain a high performance classifier. Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. We present a comprehensive study comparing supervised and unsupervised methods with our approach on two datasets composed of daily living activities. The results showed that it is possible to reduce the required annotated data by more than 89% while still maintaining an accurate model performance.

scholarly journals Cellar: Interactive single cell data annotation tool

2021 ◽  
Author(s):  
Euxhen Hasanaj ◽  
Jingtao Wang ◽  
Arjun Sarathi ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Software Tool ◽  
Cell Types ◽  
Annotation Tool ◽  
Interactive Software ◽  
Cell Level ◽  
Data Annotation ◽  
Single Cell Sequencing ◽  
Molecular Signals ◽  
Cell Data

AbstractSeveral recent technologies and platforms enable the profiling of various molecular signals at the single-cell level. A key question for all studies using such data is the assignment of cell types. To improve the ability to correctly assign cell types in single and multi-omics sequencing and imaging single-cell studies, we developed Cellar. This interactive software tool supports all steps in the analysis and assignment process. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is freely available and includes several annotated reference HuBMAP datasets.Availabilityhttps://data.test.hubmapconsortium.org/app/cellar

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Automated MS/MS data annotation: CASMI experiences

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
S Bertrand ◽  
Y Guitton ◽  
O Grovel ◽  
C Roullier
Keyword(s):  
Data Annotation
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