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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2017: 148-148c, 2017 Jul.
Article in English | MEDLINE | ID: mdl-29059831

ABSTRACT

In this paper, we present the evaluation of a new smart shoe capable of performing gait analysis in real time. The system is exclusively based on accelerometers which minimizes the power consumption. The estimated parameters are activity class (rest/walk/run), step cadence, ground contact time, foot impact (zone, strength, and balance), forward distance, and speed. The different parameters have been validated with a customized database of 26 subjects on a treadmill and video data labeled manually. Key measures for running analysis such as the cadence is retrieved with a maximum error of 2%, and the ground contact time with an average error of 3.25%. The classification of the foot impact zone achieves a precision between 72% and 91% depending of the running style. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.


Subject(s)
Gait , Accelerometry , Biomechanical Phenomena , Foot , Humans , Running , Shoes
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 4743-4746, 2016 Aug.
Article in English | MEDLINE | ID: mdl-28325014

ABSTRACT

This article presents the performance results of a novel algorithm for swimming analysis in real-time within a low-power wrist-worn device. The estimated parameters are: lap count, stroke count, time in lap, total swimming time, pace/speed per lap, total swam distance, and swimming efficiency (SWOLF). In addition, several swimming styles are automatically detected. Results were obtained using a database composed of 13 different swimmers spanning 646 laps and 858.78 min of total swam time. The final precision achieved in lap detection ranges between 99.7% and 100%, and the classification of the different swimming styles reached a sensitivity and specificity above 98%. We demonstrate that a swimmers performance can be fully analyzed with the smart bracelet containing the novel algorithm. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.


Subject(s)
Swimming/physiology , Adult , Algorithms , Computer Systems , Female , Humans , Male , Middle Aged , Wrist/physiology
3.
J Sports Med Phys Fitness ; 47(1): 13-7, 2007 Mar.
Article in English | MEDLINE | ID: mdl-17369792

ABSTRACT

AIM: The purpose of this study was to compare strength differences between 2 groups of untrained women, who performed a single set of the leg press exercise once or twice per week. METHODS: Twenty-one women were divided randomly into 2 groups: Group 1 (n=10) performed a single set of the leg press exercise once per week, while Group 2 (n=11) performed a single set of the leg press exercise twice per week for a period of 8 weeks. Throughout the duration of the study, an amount of resistance was utilized that allowed for a single set of 6 to 10 repetitions to muscular failure. At the conclusion of the study, subjects were tested for their 6-RM strength. A 2x2 ANOVA was used to compare strength differences. The a level was set at 0.05 in order for differences to be considered significant. RESULTS: The 2x2 ANOVA demonstrated that strength increases were significant between tests (P=0.0001), but not significant between groups (P=0.757). CONCLUSIONS: These results indicate that performing a single set of the leg press once or twice per week results in statistically similar strength gains in untrained women.


Subject(s)
Leg/physiology , Muscle Strength/physiology , Muscle, Skeletal/physiology , Physical Education and Training/methods , Adult , Analysis of Variance , Female , Humans , Muscle Fatigue/physiology
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