1 research outputs found
Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones
Smartphones have become an essential part of our lives.
Especially its computing power and its current specifications
make a modern smartphone even more powerful than the
computers NASA used to send astronauts to the moon.
Equipped with various integrated sensors, a modern
smartphone can be leveraged for lots of smart applications.
In this paper, we investigate the possibility of using a
unmodified commercial off-the-shelf (COTS) smartphone to
recognize 8 different workout exercises. App-based workout
has become popular in the last few years. People do not
need to go to the gym to practice. The advantage of using a
mobile device is, that you can practice anywhere at anytime.
In this work, we turned a COTS smartphone to an active
sonar device to leverage the echo reflected from exercising
movement close to the device. By conducting a test study
with 14 participants performing these eight exercises, we
show first results for cross person evaluation and the
generalization ability of our inference models on unseen
participants. A bidirectional LSTM model achieved an overall
F1 score of 88.86 % for the cross subject case and 79.52 %
for the holdout participants evaluation. Similar good results
can be achieved by a VGG16 fine-tuned model in
comparison to a 2D-CNN architecture trained from scratch