Heikki Ailisto

Dr. Heikki Ailisto


Research Focus & Metrics

Academic Career Length

42Years (1985-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 October 2006
Verified
Conference Paper
Closed access
JMJ. Mantyjarvi
J. Mantyjarvi
MLM. Lindholm
M. Lindholm
EVE. Vildjiounaite
E. Vildjiounaite
SMS.-M. Makela
S.-M. Makela
HAHeikki Ailisto
Heikki Ailisto
Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7%) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10%, 18% and 19%, respectively.

28 March 2005
Verified
Conference Paper
Closed access
PDF file
HAHeikki Ailisto
Heikki Ailisto
MLMikko Lindholm
Mikko Lindholm
JMJani Mantyjarvi
Jani Mantyjarvi
EVElena Vildjiounaite
Elena Vildjiounaite
SMSatu-Marja Makela
Satu-Marja Makela
Protecting portable devices is becoming more important, not only because of the value of the devices themselves, but for the value of the data in them and their capability for transactions, including m-commerce and m-banking. An unobtrusive and natural method for identifying the carrier of portable devices is presented. The method uses acceleration signals produced by sensors embedded in the portable device. When the user carries the device, the acceleration signal is compared with the stored template signal. The method consists of finding individual steps, normalizing and averaging them, aligning them with the template and computing cross-correlation, which is used as a measure of similarity. Equal Error Rate of 6.4% is achieved in tentative experiments with 36 test subjects.

1 April 2018 in Technological Forecasting and Social Change
Verified
Journal Article
Open access
KKKalevi Kilkki
Kalevi Kilkki
MMMartti Mäntylä
Martti Mäntylä
KKKimmo Karhu
Kimmo Karhu
HHHeikki Hämmäinen
Heikki Hämmäinen
HAHeikki Ailisto
Heikki Ailisto

1 April 2006 in Pattern Recognition Letters
Verified
Journal Article
Closed access
HAHeikki Ailisto
Heikki Ailisto
EVElena Vildjiounaite
Elena Vildjiounaite
MLMikko Lindholm
Mikko Lindholm
SMSatu-Marja Mäkelä
Satu-Marja Mäkelä
JPJohannes Peltola
Johannes Peltola

9 September 2007
Verified
Conference Paper
Open access
PDF file
JHJuha Häikiö
Juha Häikiö
AWArto Wallin
Arto Wallin
MIMinna Isomursu
Minna Isomursu
HAHeikki Ailisto
Heikki Ailisto
TMTapio Matinmikko
Tapio Matinmikko
THTua Huomo
Tua Huomo
This paper reports the results of a field experiment where a Near Field Communication (NFC) enabled mobile phone was used as a user interface element so as to enable home-dwelling elderly people to choose their meals to be delivered by means of a home care service. The primary research focus was on examining the suitability of a touch-based user interface in the everyday life activities of elderly users. The eight-week experiment took place in the autumn of 2006. The results show that the touch-based user interface was easy to learn and adopt and the users were able to successfully use it regardless of their physical or cognitive weaknesses.

1 January 2006
Verified
Book Chapter
Closed access
PDF file
EVElena Vildjiounaite
Elena Vildjiounaite
SMSatu-Marja Mäkelä
Satu-Marja Mäkelä
MLMikko Lindholm
Mikko Lindholm
RRReima Riihimäki
Reima Riihimäki
VKVesa Kyllönen
Vesa Kyllönen
HAHeikki Ailisto
Heikki Ailisto