Efficient accelerometer-based swimming exercise tracking

Siirtola, Pekka; Laurinen, Perttu; Röning, Juha; Kinnunen, Hannu

doi:10.1109/cidm.2011.5949430

Cited by 68 publications

(57 citation statements)

References 11 publications

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“…Thus, for example, the flip turn data was achieved only from professional swimmers. The data was collected at 50Hz but because of the higher recognition rate presented with lower frequency in [13] the data was sampled into 10Hz. The daily activity data set included data from 18 somewhat overlapping activities from 21 persons.…”

Section: Data Setsmentioning

confidence: 99%

Avoiding Bias in Classification Accuracy - A Case Study for Activity Recognition

Koskimäki

2015

2015 IEEE Symposium Series on Computational Intelligence

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The amount of studies on classification of human characteristics based on measured individual signals has increased rapidly. In wearable sensors based activity recognition a common policy is to report human independent recognition results using leave-one-person-out cross-validation scheme. This can be a suitable solution when feature or model parameter selection is not needed or it is done outside the validation scheme. Unfortunately, this is not always the reality. Thus in this article it is studied how the train-validate-test approach changes the recognition rates compared to basic leave-one-out cross-validation approach. Results of three different ways to perform the trainvalidate-test is presented: 1) single division to training and testing data, 2) 10-fold division to training and testing data, and 3) double leave-one-person-out cross-validation. In this article, it is shown that the best classifier or feature set selected based on the training and validation data using basic leave-one-out approach does not always perform best within independent testing data. Nevertheless, a larger bias to results can be achieved using single division or even 10-fold division into separate training and testing data. Thus it is stated that the double leave-one-person-out is the most robust version for reporting classification rates in future studies of activity recognition as well as other areas where human signals are used.

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Section: Data Setsmentioning

confidence: 99%

Avoiding Bias in Classification Accuracy - A Case Study for Activity Recognition

Koskimäki

2015

2015 IEEE Symposium Series on Computational Intelligence

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“…A Google Nexus 4 smartphone is placed in a waterproof pouch and strapped tightly onto the central lower back of the swimmer using a simple waist strap and pouch. This position was chosen, as in previous IMU research [1], [3], [5], [13], [14] because it offers a predictable orientation relative to the base of the swimming pool, and allows orientation sensing to reliably detect the direction of swimming. Fig.…”

Section: Magnetic Field In a Swimming Poolmentioning

confidence: 99%

Magnetic Field Swimmer Positioning

Marshall

2015

IEEE Sensors J.

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Abstract-Several projects have tracked the movement of swimmers in pools using body worn inertial measurement units. In swimming, inertial sensing is subject to large amounts of drift and accumulated error which can only be corrected for after a complete length has been swum. In this article, we present a new method for tracking swimmers by detecting variations in the magnetic field caused by the structure of pools. This method is complementary to inertial positioning, as it allows the direct extraction of position without requiring post-processing, and unlike inertial sensing which loses accuracy over time, magnetic field tracking becomes increasingly accurate towards the end of a length.

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“…Another interesting trend in activity recognition is the utilization of the developed methods into sport activity recognition in general level [12] or in sport specific areas like in swimming [13]. A very interesting application area in sports is related to gym exercises.…”

Section: Introductionmentioning

confidence: 99%

Recognizing gym exercises using acceleration data from wearable sensors

Koskimäki

Siirtola

2014

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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The activity recognition approaches can be used for entertainment, to give people information about their own behavior, and to monitor and supervise people through their actions. Thus, it is a natural consequence of that fact that the amount of wearable sensors based studies has increased as well, and new applications of activity recognition are being invented in the process. In this study, gym data, including 36 different exercise classes, is used aiming in the future to create automatic activity diaries showing reliably to end users how many sets of given exercise have been performed. The actual recognition is divided into two different steps. In the first step, activity recognition of certain time intervals is performed and in the second step the state-machine approach is used to decide when actual events (sets in gym data) were performed. The results showed that when recognizing different exercise sets from the same occasion (sequential exercise sets), on average, over 96 percent window-wise true positive rate can be achieved, and moreover, all the exercise events can be discovered using the state-machine approach. When using a separate validation test set, the accuracies decreased significantly for some classes, but even in this case, all the different sets were discovered for 26 different classes.

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Efficient accelerometer-based swimming exercise tracking

Cited by 68 publications

References 11 publications

Avoiding Bias in Classification Accuracy - A Case Study for Activity Recognition

Avoiding Bias in Classification Accuracy - A Case Study for Activity Recognition

Magnetic Field Swimmer Positioning

Recognizing gym exercises using acceleration data from wearable sensors

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