PMData: A Sports Logging Dataset

Vajira Thambawita; Steven Hicks; Hannah Borgli; Håkon Stensland; Debesh Jha; Martin Kristoffer Svensen; Svein Arne  Pettersen; Dag  Johansen; Hårvard  Dagenborg Johansen; Susann Dahl Pettersen; Simon  Nordvang; Sigurd  Pedersen; Anders T. Gjerdum; Tor-Morten Grønli; Per Morten  Fredriksen; Ragnhild  Eg; Kjeld S. Hansen; Siri  Fagernes; Christine Claudi; Christine  Claudi; Andreas Biørn-Hansen; Duc  Tien Nguyen; Tomas Kupka; Hugo Lewi Hammer; Ramesh C. Jain; Michael  Riegler; Pål  Halvorsen

doi:10.1145/3339825.3394926

PMData: A Sports Logging Dataset

Vajira Thambawita, Steven Hicks, Hannah Borgli, Håkon Stensland, Debesh Jha, Martin Kristoffer Svensen, Svein Arne Pettersen, Dag Johansen, Hårvard Dagenborg Johansen, Susann Dahl Pettersen, Simon Nordvang, Sigurd Pedersen, Anders T. Gjerdum, Tor-Morten Grønli, Per Morten Fredriksen, Ragnhild Eg, Kjeld S. Hansen, Siri Fagernes, Christine Claudi, Christine ClaudiAndreas Biørn-Hansen, Duc Tien Nguyen, Tomas Kupka, Hugo Lewi Hammer, Ramesh C. Jain, Michael Riegler, Pål Halvorsen

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Abstract

In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. PMData combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

Originalsprog	Engelsk
Titel	MMSys '20 : Proceedings of the 11th ACM Multimedia Systems Conference
Redaktører	Laura Toni, Ali C. Begen
Antal sider	6
Udgivelsessted	New York
Forlag	Association for Computing Machinery
Publikationsdato	27 maj 2020
Sider	231-236
Kapitel	6
ISBN (Trykt)	9781450368452
ISBN (Elektronisk)	9781450368452
DOI	https://doi.org/10.1145/3339825.3394926
Status	Udgivet - 27 maj 2020
Udgivet eksternt	Ja
Begivenhed	The 11th ACM Multimedia Systems Online Conference - , WWW Varighed: 8 jun. 2020 → 11 jun. 2020 Konferencens nummer: 11 https://2020.acmmmsys.org/

Konference

Konference	The 11th ACM Multimedia Systems Online Conference
Nummer	11
Land/Område	WWW
Periode	08/06/2020 → 11/06/2020
Internetadresse	https://2020.acmmmsys.org/

Emneord

Multimedia dataset
Neural networks
Machine learning
Sports logging
Sensor data
Questionnaires
Food pictures

Adgang til dokumentet

10.1145/3339825.3394926Licens: Ikke-specificeret

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Citationsformater

Thambawita, V., Hicks, S., Borgli, H., Stensland, H., Jha, D., Kristoffer Svensen, M., Pettersen, S. A., Johansen, D., Dagenborg Johansen, H., Pettersen, S. D., Nordvang, S., Pedersen, S., Gjerdum, A. T., Grønli, T.-M., Fredriksen, P. M., Eg, R., Hansen, K. S., Fagernes, S., Claudi, C., ... Halvorsen, P. (2020). PMData: A Sports Logging Dataset. I L. Toni, & A. C. Begen (red.), MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference (s. 231-236). Association for Computing Machinery. https://doi.org/10.1145/3339825.3394926

@inproceedings{f8c09bcd636c498ead691a78487ccc0b,

title = "PMData: A Sports Logging Dataset",

abstract = "In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. PMData combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.",

keywords = "Multimedia dataset, Neural networks, Machine learning, Sports logging, Sensor data, Questionnaires, Food pictures, Multimedia dataset, Neural networks, Machine learning, Sports logging, Sensor data, Questionnaires, Food pictures",

author = "Vajira Thambawita and Steven Hicks and Hannah Borgli and H{\aa}kon Stensland and Debesh Jha and {Kristoffer Svensen}, Martin and Pettersen, {Svein Arne} and Dag Johansen and {Dagenborg Johansen}, H{\aa}rvard and Pettersen, {Susann Dahl} and Simon Nordvang and Sigurd Pedersen and Gjerdum, {Anders T.} and Tor-Morten Gr{\o}nli and Fredriksen, {Per Morten} and Ragnhild Eg and Hansen, {Kjeld S.} and Siri Fagernes and Christine Claudi and Christine Claudi and Andreas Bi{\o}rn-Hansen and {Tien Nguyen}, Duc and Tomas Kupka and {Lewi Hammer}, Hugo and Jain, {Ramesh C.} and Michael Riegler and P{\aa}l Halvorsen",

year = "2020",

month = may,

day = "27",

doi = "10.1145/3339825.3394926",

language = "English",

isbn = "9781450368452",

pages = "231--236",

editor = "Laura Toni and Begen, {Ali C.}",

booktitle = "MMSys '20",

publisher = "Association for Computing Machinery",

address = "United States",

note = "The 11th ACM Multimedia Systems Online Conference ; Conference date: 08-06-2020 Through 11-06-2020",

url = "https://2020.acmmmsys.org/",

}

Thambawita, V, Hicks, S, Borgli, H, Stensland, H, Jha, D, Kristoffer Svensen, M, Pettersen, SA, Johansen, D, Dagenborg Johansen, H, Pettersen, SD, Nordvang, S, Pedersen, S, Gjerdum, AT, Grønli, T-M, Fredriksen, PM, Eg, R, Hansen, KS, Fagernes, S, Claudi, C, Claudi, C, Biørn-Hansen, A, Tien Nguyen, D, Kupka, T, Lewi Hammer, H, Jain, RC, Riegler, M & Halvorsen, P 2020, PMData: A Sports Logging Dataset. i L Toni & AC Begen (red), MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference. Association for Computing Machinery, New York, s. 231-236, The 11th ACM Multimedia Systems Online Conference, WWW, 08/06/2020. https://doi.org/10.1145/3339825.3394926

TY - GEN

T1 - PMData

T2 - The 11th ACM Multimedia Systems Online Conference

AU - Thambawita, Vajira

AU - Hicks, Steven

AU - Borgli, Hannah

AU - Stensland, Håkon

AU - Jha, Debesh

AU - Kristoffer Svensen, Martin

AU - Pettersen, Svein Arne

AU - Johansen, Dag

AU - Dagenborg Johansen, Hårvard

AU - Pettersen, Susann Dahl

AU - Nordvang, Simon

AU - Pedersen, Sigurd

AU - Gjerdum, Anders T.

AU - Grønli, Tor-Morten

AU - Fredriksen, Per Morten

AU - Eg, Ragnhild

AU - Hansen, Kjeld S.

AU - Fagernes, Siri

AU - Claudi, Christine

AU - Biørn-Hansen, Andreas

AU - Tien Nguyen, Duc

AU - Kupka, Tomas

AU - Lewi Hammer, Hugo

AU - Jain, Ramesh C.

AU - Riegler, Michael

AU - Halvorsen, Pål

N1 - Conference code: 11

PY - 2020/5/27

Y1 - 2020/5/27

N2 - In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. PMData combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

AB - In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. PMData combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

KW - Multimedia dataset

KW - Neural networks

KW - Machine learning

KW - Sports logging

KW - Sensor data

KW - Questionnaires

KW - Food pictures

KW - Multimedia dataset

KW - Neural networks

KW - Machine learning

KW - Sports logging

KW - Sensor data

KW - Questionnaires

KW - Food pictures

U2 - 10.1145/3339825.3394926

DO - 10.1145/3339825.3394926

M3 - Article in proceedings

SN - 9781450368452

SP - 231

EP - 236

BT - MMSys '20

A2 - Toni, Laura

A2 - Begen, Ali C.

PB - Association for Computing Machinery

CY - New York

Y2 - 8 June 2020 through 11 June 2020

ER -