We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace,

such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done

and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person's root position is available, an extended version of our algorithm can use this information to correct positional drift

Kelly, Philip

O'Connor, Noel E.

Ó Conaire, Ciarán

English

We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace,
such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done
and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person\u27s root position is available, an extended version of our algorithm can use this information to correct positional drift

O\u27Connor, Noel E.

Irish Universities

Human motion reconstruction using wearable accelerometers

We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace,\ud
such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done\ud
and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person's root position is available, an extended version of our algorithm can use this information to correct positional drift

Name not available

Eurographics/ ACM SIGGRAPH Symposium on Computer Animation (2010) Posters and DemosHuman Motion Reconstruction using WearableAccelerometersPhilip Kelly 1, Ciarán Ó Conaire 1, Jessica Hodgins 2 and Noel E. O’Connor 11Clarity: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland2Disney Research Pittsburgh, Pittsburgh, PA, USAAbstractWe address the problem of capturing human motion in scenarios where the use of a traditional optical motioncapture system is impractical. Such scenarios are relatively commonplace, such as in large spaces, outdoors orat competitive sporting events, where the limitations of such systems are apparent: the small physical area wheremotion capture can be done and the lack of robustness to lighting changes and occlusions. In this paper, weadvocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to thisend we outline a system that is more portable than traditional optical motion capture systems, whilst producingnaturalistic motion. Additionally, if information on the person’s root position is available, an extended version ofour algorithm can use this information to correct positional drift.Categories and Subject Descriptors (according to ACM CCS): INFORMATION INTERFACES AND PRESEN-TATION [H.5.1]: Multimedia Information Systems —Animations COMPUTER GRAPHICS [I.3.7]: Three-Dimensional Graphics and Realism—Animation1. IntroductionFor a number of years, optical motion capture systems haveprovided sports scientists, coaches and athletes with impor-tant data about an athlete’s performance that is difficult toelicit with the naked eye or even via high speed video cap-ture. Although very accurate, optical motion capture systemsare cumbersome for capturing precise, complex and rapidathletic movement in some scenarios. Athletes, in particular,often move very quickly through large spatial volumes thatmay be difficult to densely and safely populate with cameras.They also often perform outdoors where the lighting condi-tions cannot be easily controlled. Additionally, occlusion ofmarkers, due to sports equipment or other players, can causetracking difficulties. Artifacts in reconstructed motion dueto occlusions may be overcome by manual intervention andcorrection, but for time-critical applications, such as instantreplays of the athlete’s 3D motion for sports broadcastingpurposes, manual interventions may be unfeasible.In this paper, we outline a system to address these limita-tions. Our goal is to produce a portable system that can op-erate in large sporting areas such as outdoor arenas, wheretraditional motion capture systems would have problems. Inaddition, we would like to make the system relatively cheap,unobtrusive to wear and, of course, obtain reconstructed mo-tion to as high a degree of accuracy as possible. The focus ofthis work is on the specific application of novel visualisationof athlete performance within sports broadcasting scenarios.2. OverviewFigure 2 illustrates an overview of the different stages of theproposed system: (a) an offline stage, (b) a pre-capture stageand (c) the motion reconstruction stage. In the offline stage, amotion capture database is created that contains a sample setof the types of motions that we expect a player to performat capture time. Using this, a motion graph is constructed,encoding how captured clips may be reassembled in differ-ent ways. Novel motions can be obtained by traversing thegraph. The pre-capture stage of the system tailors the motiongraph to different placements of accelerometers on players,using virtual accelerometers to generate accelerometer datafor each motion in the motion graph The online stage of thesystem reproduces novel motions by finding paths in the mo-P. Kelly, C. Ó Conaire, J. K. Hodgins & N. E. O’Connor / Human Motion Reconstruction using Wearable AccelerometersFigure 1: Tennis reconstruction from six accelerometers.Figure 2: System Overview.tion graph that would produce similar acceleration readingsto those measured at capture time. Using a dynamic pro-gramming (DP) solution to find the optimal path through thegraph, this results in a sequence of poses whose accelera-tions most closely match the recorded accelerometer values.Noting that the root position in the motion produced by thismethod can drift away from the correct body position andorientation, an alternative approach can also be applied inthe online stage, that attempts to correct this using player(a) (b)Figure 3: Joint angle error and positional error comparison.position information from an external source. Any localisa-tion method can be used to supply this positional information(e.g. image-processing from calibrated cameras [CKCO09],GPS, UbiSense, etc). For this second graph search tech-nique, the proposed framework temporally segments a cap-ture session into action and non-action segments. The DPsolution reconstructs motion during action segments, result-ing in highly accurate motion at important times, and a sec-ond search strategy, using A* search, attempts to correct theposition and orientation drift of the actor during non-actiontimes.3. Experimental ResultsFor quantitative evaluation of the proposed technique,we benchmarked the two search algorithms – using 16tennis motion sequences of 6 different types (S=Serve,F=Forehand, B=Backhand, M=Motion, H=move and Hit,f=Freestyle) – against prior work in action capture using ac-celerometers [SH08] and ground truth motion capture data –see Figure 3. The results show that our DP approach outper-forms Slyper in almost all cases (or is very close), while theA∗ search manages to keep a relatively low error, while alsoimproving upon the positional error of both DP and Slyper.References[CKCO09] CONAIRE C. O., KELLY P., CONNAGHAN D.,O’CONNOR. N. E.: Tennissense: A platform for extracting se-mantic information from multi-camera tennis data. In DSP 2009(2009), pp. 1062–1067.[SH08] SLYPER R., HODGINS J.: Action capture with ac-celerometers. In 2008 ACM SIGGRAPH / Eurographics Sym-posium on Computer Animation (July 2008).

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HUMAN MOTION RECONSTRUCTION USING WEARABLEACCELEROMETERSP. Kelly1, C. ´O Conaire1, J. K. Hodgins2 & N. E. O’Connor11CLARITY: Centre for Sensor Web Technologies, Dublin City University, Ireland2Disney Research Pittsburgh, Pittsburgh, PA, USAIntroduction• Focus on the specific application of visualisation of athlete performance in sports broadcasting scenarios• Currently several days manual work required for 3D reconstruction of a play in ESPN’s Virtual Playbook• Optical motion capture systems – traditional approach for providing precise athlete performance data– Although very accurate, can be are cumbersome/impractical in some scenarios∗ Large spatial volumes; Areas with uncontrolled lighting; Time-critical applications; Manual correc-tion of artifacts may be necessary due when incorrect tracking/occlusion of markers occurs.• IMU based systems– Can suffer from motion drift; Size of units would impair the performance of high level athletes;– Speed of movement may result in poor motion reconstruction for the most critical movements.• We focus on placing only accelerometers on the body, as they can be unobtrusively sown into clothing• Our goal is to produce a cheap, unobtrusive and portable motion capture system that can– Operate in large sporting areas, such as outdoor arenas;– Obtain reconstructed motion to as high a degree of accuracy as possible;– Be used in time-critical applications, such as instant replays in sports broadcasting.Overview• The proposed system has three main stages;1. Offline stage• Motion capture database is created– Database contains a sample set of the types of motions that we expect to be performed at capture time• Motion graph constructed using database– Graph encodes how captured database clips may be reassembled in different ways2. Pre-capture stage• Tailors the motion graph to different placements of accelerometers on players• Uses virtual accelerometers to generate accelerometer data for each node in the motion graph3. Online motion reconstruction stage• Reproduces novel motions by finding paths in the motion graph that would produce similar accelerationreadings to those measured at capture time• Dynamic Programming (DP) search strategy– Finds the optimal path through the graph– Results in a sequence of poses whose accelerations closely match the recorded accelerometer values• DP/A* search strategy– Incorporates root position and orientation into the search strategy– Positional information can be obtained from any external source– Segments a capture session into action and non-action segments∗ DP solution reconstructs motion during action segments∗ A* search strategy corrects the position and orientation drift of the actor during non-action timesExperimental ResultsS S S F F B B M M M H H f f f f 0510152025303540SequenceMean Joint Angle Error (degrees)  Slyper HaarViterbi (DP)A* search(a)S S S F F B B M M M H H f f f f 012345678SequenceMean Position Error (metres)  Slyper HaarViterbi (DP)A* search(b)Figure 1: Joint angle error and positional error comparison.• Quantitative evaluation using 16 tennis motion sequences of 6 different types – see Figure 1– S=Serve, F=Forehand, B=Backhand, M=Motion, H=move and Hit, f=Freestyle– Slyper Haar: Prior work in action capture using accelerometers [1]– DP / A* Search: The two search strategies outlined in this work– Groundtruth motion data captured via optical capture system• DP approach outperforms [1] in almost all cases (or is very close)• A* search keeps a relatively low error, while also improving upon the positional error of both DP and [1]AcknowledgementsThis work is supported by Science Foundation Ireland under grant 07/CE/I1147 and by the Tyndall NationalInstitute under NAP Grant 209. The authors would also like to thank Moshe Mahler and Justin Maceyfrom the Computer Graphics Lab in Carnegie Mellon University for contributing their time and considerableexpertise in the areas of data capture and visualisation.References[1] R. Slyper and J. Hodgins. Action capture with accelerometers. In 2008 ACM SIGGRAPH / Eurographics Symposium onComputer Animation, July 2008.

Action capture with accelerometers.

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Human motion reconstruction using wearable accelerometers

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