  Human activity recognition based on wearable sensor data has been an
attractive research topic due to its application in areas such as healthcare
and smart environments. In this context, many works have presented remarkable
results using accelerometer, gyroscope and magnetometer data to represent the
activities categories. However, current studies do not consider important
issues that lead to skewed results, making it hard to assess the quality of
sensor-based human activity recognition and preventing a direct comparison of
previous works. These issues include the samples generation processes and the
validation protocols used. We emphasize that in other research areas, such as
image classification and object detection, these issues are already
well-defined, which brings more efforts towards the application. Inspired by
this, we conduct an extensive set of experiments that analyze different sample
generation processes and validation protocols to indicate the vulnerable points
in human activity recognition based on wearable sensor data. For this purpose,
we implement and evaluate several top-performance methods, ranging from
handcrafted-based approaches to convolutional neural networks. According to our
study, most of the experimental evaluations that are currently employed are not
adequate to perform the activity recognition in the context of wearable sensor
data, in which the recognition accuracy drops considerably when compared to an
appropriate evaluation approach. To the best of our knowledge, this is the
first study that tackles essential issues that compromise the understanding of
the performance in human activity recognition based on wearable sensor data.

Computer Vision and Pattern Recognition