112 research outputs found
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
Identifying cross country skiing techniques using power meters in ski poles
Power meters are becoming a widely used tool for measuring training and
racing effort in cycling, and are now spreading also to other sports. This
means that increasing volumes of data can be collected from athletes, with the
aim of helping coaches and athletes analyse and understanding training load,
racing efforts, technique etc. In this project, we have collaborated with
Skisens AB, a company producing handles for cross country ski poles equipped
with power meters. We have conducted a pilot study in the use of machine
learning techniques on data from Skisens poles to identify which "gear" a skier
is using (double poling or gears 2-4 in skating), based only on the sensor data
from the ski poles. The dataset for this pilot study contained labelled
time-series data from three individual skiers using four different gears
recorded in varied locations and varied terrain. We systematically evaluated a
number of machine learning techniques based on neural networks with best
results obtained by a LSTM network (accuracy of 95% correctly classified
strokes), when a subset of data from all three skiers was used for training. As
expected, accuracy dropped to 78% when the model was trained on data from only
two skiers and tested on the third. To achieve better generalisation to
individuals not appearing in the training set more data is required, which is
ongoing work.Comment: Presented at the Norwegian Artificial Intelligence Symposium 201
A Machine Learning based Activity Recognition for Ambient Assisted Living
Ambient assisted living (AAL) technology is of considerable interest in supporting the independence and quality of life of older adults. As such, it is a core focus of the emerging field of gerontechnology, which considers how technological innovation can aid health and well-being in older age. Human activity recognition plays a vital role in AAL. Successful identification of human activity is crucial for any assistive care services for elderly people living alone in a home. In this paper, a method for activity recognition is proposed which recognizes or classifies activities based on sensor data. The method uses most trending algorithm in deep learning domain, i.e. LSTM to build the model .The proposed method is evaluated using a well known activity sensor dataset
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
- …