Alternative techniques for the efficient acquisition of haptic data
Identifieur interne :
000178 ( PascalFrancis/Curation );
précédent :
000177;
suivant :
000179
Alternative techniques for the efficient acquisition of haptic data
Auteurs : C. Shahabi [
États-Unis] ;
M. R. Kolahdouzan ;
G. Barish ;
R. Zimmermann ;
D. Yao ;
K. Fu ;
L. ZhangSource :
-
Performance Evaluation Review [ 0163-5999 ] ; 2001.
RBID : Pascal:02-0028025
Descripteurs français
- Pascal (Inist)
- Théorie,
Interface haptique,
Système temps réel,
Equipement stockage donnée,
Langage requête,
Analyse information,
Système commande échantillonné,
Enregistrement donnée,
Système commande adaptative,
Capteur,
Boucle réaction,
Saisie donnée.
English descriptors
- KwdEn :
- Adaptive control systems,
Data acquisition,
Data recording,
Data storage equipment,
Feedback,
Haptic data acquisition,
Haptic interfaces,
Immersidata,
Immersive technologies,
Information analysis,
Query languages,
Real time systems,
Sampled data control systems,
Sensors,
Theory.
Abstract
Immersive environments are those that surround users in an artificial world. These environments consist of a composition of various types of immersidata: unique data types that are combined to render a virtual experience. Acquisition, for storage and future querying, of information describing sessions in these environments is challenging because of the real-time demands and sizeable amounts of data to be managed. In this paper, we summarize a comparison of techniques for achieving the efficient acquisition of one type of immersidata, the haptic data type, which describes the movement, rotation, and force associated with user-directed objects in an immersive environment. In addition to describing a general process for real-time sampling and recording of this type of data, we propose three distinct sampling strategies: fixed, grouped, and adaptive. We conducted several experiments with a real haptic device and found that there are tradeoffs between the accuracy, efficiency, and complexity of implementation for each of the proposed techniques. While it is possible to use any of these approaches for real-time haptic data acquisition, we found that an adaptive sampling strategy provided the most efficiency without significant loss in accuracy. As immersive environments become more complex and contain more haptic sensors, techniques such as adaptive sampling can be useful for improving scalability of real-time data acquisition.
| pA |
| A01 | 01 | 1 | | @0 0163-5999 |
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| A06 | | | | @2 1 |
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| A08 | 01 | 1 | ENG | @1 Alternative techniques for the efficient acquisition of haptic data |
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| A11 | 01 | 1 | | @1 SHAHABI (C.) |
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| A11 | 02 | 1 | | @1 KOLAHDOUZAN (M. R.) |
|---|
| A11 | 03 | 1 | | @1 BARISH (G.) |
|---|
| A11 | 04 | 1 | | @1 ZIMMERMANN (R.) |
|---|
| A11 | 05 | 1 | | @1 YAO (D.) |
|---|
| A11 | 06 | 1 | | @1 FU (K.) |
|---|
| A11 | 07 | 1 | | @1 ZHANG (L.) |
|---|
| A14 | 01 | | | @1 Integrated Media Systems Center Department of Computer Science University of Southern California @2 Los Angeles, CA 90089 @3 USA @Z 1 aut. |
|---|
| A17 | 01 | 1 | | @1 ACM SIGMETRICS @3 INC @9 patr. |
|---|
| A17 | 02 | 1 | | @1 IFIP @3 INC @9 patr. |
|---|
| A20 | | | | @1 334-335 |
|---|
| A21 | | | | @1 2001 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A43 | 01 | | | @1 INIST @2 18401 |
|---|
| A44 | | | | @0 A100 |
|---|
| A45 | | | | @0 1 Refs. |
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| A47 | 01 | 1 | | @0 02-0028025 |
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|---|
| A61 | | | | @0 A |
|---|
| A64 | 01 | 1 | | @0 Performance Evaluation Review |
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| A66 | 01 | | | @0 USA |
|---|
| C01 | 01 | | ENG | @0 Immersive environments are those that surround users in an artificial world. These environments consist of a composition of various types of immersidata: unique data types that are combined to render a virtual experience. Acquisition, for storage and future querying, of information describing sessions in these environments is challenging because of the real-time demands and sizeable amounts of data to be managed. In this paper, we summarize a comparison of techniques for achieving the efficient acquisition of one type of immersidata, the haptic data type, which describes the movement, rotation, and force associated with user-directed objects in an immersive environment. In addition to describing a general process for real-time sampling and recording of this type of data, we propose three distinct sampling strategies: fixed, grouped, and adaptive. We conducted several experiments with a real haptic device and found that there are tradeoffs between the accuracy, efficiency, and complexity of implementation for each of the proposed techniques. While it is possible to use any of these approaches for real-time haptic data acquisition, we found that an adaptive sampling strategy provided the most efficiency without significant loss in accuracy. As immersive environments become more complex and contain more haptic sensors, techniques such as adaptive sampling can be useful for improving scalability of real-time data acquisition. |
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| C02 | 05 | X | | @0 001D02D |
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| C02 | 06 | X | | @0 205 |
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| C03 | 01 | 1 | ENG | @0 Immersive technologies @4 INC |
|---|
| C03 | 02 | 1 | ENG | @0 Immersidata @4 INC |
|---|
| C03 | 03 | 1 | ENG | @0 Haptic data acquisition @4 INC |
|---|
| C03 | 04 | 1 | FRE | @0 Théorie |
|---|
| C03 | 04 | 1 | ENG | @0 Theory |
|---|
| C03 | 05 | 1 | FRE | @0 Interface haptique |
|---|
| C03 | 05 | 1 | ENG | @0 Haptic interfaces |
|---|
| C03 | 06 | 1 | FRE | @0 Système temps réel |
|---|
| C03 | 06 | 1 | ENG | @0 Real time systems |
|---|
| C03 | 07 | 1 | FRE | @0 Equipement stockage donnée |
|---|
| C03 | 07 | 1 | ENG | @0 Data storage equipment |
|---|
| C03 | 08 | 1 | FRE | @0 Langage requête |
|---|
| C03 | 08 | 1 | ENG | @0 Query languages |
|---|
| C03 | 09 | 1 | FRE | @0 Analyse information |
|---|
| C03 | 09 | 1 | ENG | @0 Information analysis |
|---|
| C03 | 10 | 1 | FRE | @0 Système commande échantillonné |
|---|
| C03 | 10 | 1 | ENG | @0 Sampled data control systems |
|---|
| C03 | 11 | 1 | FRE | @0 Enregistrement donnée |
|---|
| C03 | 11 | 1 | ENG | @0 Data recording |
|---|
| C03 | 12 | 1 | FRE | @0 Système commande adaptative |
|---|
| C03 | 12 | 1 | ENG | @0 Adaptive control systems |
|---|
| C03 | 13 | 1 | FRE | @0 Capteur |
|---|
| C03 | 13 | 1 | ENG | @0 Sensors |
|---|
| C03 | 14 | 1 | FRE | @0 Boucle réaction |
|---|
| C03 | 14 | 1 | ENG | @0 Feedback |
|---|
| C03 | 15 | 1 | FRE | @0 Saisie donnée @3 P |
|---|
| C03 | 15 | 1 | ENG | @0 Data acquisition @3 P |
|---|
| N21 | | | | @1 014 |
|---|
|
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| pR |
| A30 | 01 | 1 | ENG | @1 Joint International Conference on Measurement and Modeling of Computer Systems @3 Cambridge, MA, United States @4 1901-06-16/1901-06-20 |
|---|
|
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Le document en format XML
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<term>Haptic data acquisition</term>
<term>Haptic interfaces</term>
<term>Immersidata</term>
<term>Immersive technologies</term>
<term>Information analysis</term>
<term>Query languages</term>
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<term>Système temps réel</term>
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</keywords><front><div type="abstract" xml:lang="en">Immersive environments are those that surround users in an artificial world. These environments consist of a composition of various types of immersidata: unique data types that are combined to render a virtual experience. Acquisition, for storage and future querying, of information describing sessions in these environments is challenging because of the real-time demands and sizeable amounts of data to be managed. In this paper, we summarize a comparison of techniques for achieving the efficient acquisition of one type of immersidata, the haptic data type, which describes the movement, rotation, and force associated with user-directed objects in an immersive environment. In addition to describing a general process for real-time sampling and recording of this type of data, we propose three distinct sampling strategies: fixed, grouped, and adaptive. We conducted several experiments with a real haptic device and found that there are tradeoffs between the accuracy, efficiency, and complexity of implementation for each of the proposed techniques. While it is possible to use any of these approaches for real-time haptic data acquisition, we found that an adaptive sampling strategy provided the most efficiency without significant loss in accuracy. As immersive environments become more complex and contain more haptic sensors, techniques such as adaptive sampling can be useful for improving scalability of real-time data acquisition.</div>
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