A carregar...
Publicação
Real-Time Step Detection Using Unconstrained Smartphone
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 7.61 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Nowadays smartphones are carrying more and more sensors among which are inertial
sensors. These devices provide information about the movement and forces acting on
the device, but they can also provide information about the movement of the user. Step
detection is at the core of many smartphone applications such as indoor location, virtual
reality, health and activity monitoring, and some of these require high levels of precision.
Current state of the art step detection methods rely heavily in the prediction of the
movements performed by the user and the smartphone or on methods of activity recognition
for parameter tuning. These methods are limited by the number of situations the
researchers can predict and do not consider false positive situations which occur in daily
living such as jumps or stationary movements, which in turn will contribute to lower
performances.
In this thesis, a novel unconstrained smartphone step detection method is proposed
using Convolutional Neural Networks. The model utilizes the data from the accelerometer
and gyroscope of the smartphone for step detection. For the training of the model, a
data set containing step and false step situations was built with a total of 4 smartphone
placements, 5 step activities and 2 false step activities. The model was tested using the
data from a volunteer which it has not previously seen.
The proposed model achieved an overall recall of 89.87% and an overall precision of
87.90%, while being able to distinguish step and non-step situations. The model also
revealed little difference between the performance in different smartphone placements,
indicating a strong capability towards unconstrained use. The proposed solution demonstrates
more versatility than state of the art alternatives, by presenting comparable results
without the need of parameter tuning or adjustments for the smartphone use case, potentially
allowing for better performances in free living scenarios.
Descrição
Palavras-chave
Step Detection Smartphone Sensors Convolutional Neural Networks Artificial Intelligence Deep Learning