43 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
SensX: About Sensing and Assessment of Complex Human Motion
The great success of wearables and smartphone apps for provision of extensive
physical workout instructions boosts a whole industry dealing with consumer
oriented sensors and sports equipment. But with these opportunities there are
also new challenges emerging. The unregulated distribution of instructions
about ambitious exercises enables unexperienced users to undertake demanding
workouts without professional supervision which may lead to suboptimal training
success or even serious injuries. We believe, that automated supervision and
realtime feedback during a workout may help to solve these issues. Therefore we
introduce four fundamental steps for complex human motion assessment and
present SensX, a sensor-based architecture for monitoring, recording, and
analyzing complex and multi-dimensional motion chains. We provide the results
of our preliminary study encompassing 8 different body weight exercises, 20
participants, and more than 9,220 recorded exercise repetitions. Furthermore,
insights into SensXs classification capabilities and the impact of specific
sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International
Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017,
Calabria Italy 6 pages, 5 figure
Comparing CNN and Human Crafted Features for Human Activity Recognition
Deep learning techniques such as Convolutional
Neural Networks (CNNs) have shown good results in activity
recognition. One of the advantages of using these methods resides
in their ability to generate features automatically. This ability
greatly simplifies the task of feature extraction that usually
requires domain specific knowledge, especially when using big
data where data driven approaches can lead to anti-patterns.
Despite the advantage of this approach, very little work has
been undertaken on analyzing the quality of extracted features,
and more specifically on how model architecture and parameters
affect the ability of those features to separate activity classes
in the final feature space. This work focuses on identifying the
optimal parameters for recognition of simple activities applying
this approach on both signals from inertial and audio sensors.
The paper provides the following contributions: (i) a comparison
of automatically extracted CNN features with gold standard
Human Crafted Features (HCF) is given, (ii) a comprehensive
analysis on how architecture and model parameters affect separation
of target classes in the feature space. Results are evaluated
using publicly available datasets. In particular, we achieved a
93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with
3 convolutional layers and 32 kernel size, and a 90.5% F-Score
on the DCASE 2017 development dataset, simplified for three
classes (indoor, outdoor and vehicle), using 2D CNNs with 2
convolutional layers and a 2x2 kernel size
Embedded AM-FM Signal Decomposition Algorithm for Continuous Human Activity Monitoring
AM-FM decomposition techniques have been successfully used for extracting significative features from a large variety of signals, helping realtime signal monitoring and pattern recognition, since they represent signals as a simultaneous composition of amplitude modulation and frequency modulation, where the carriers, amplitude envelopes, and the instantaneous frequencies are the features to be estimated. Human activities often involve repetitive movements, such as in running or cycling, where sinusoidal AM-FM decompositions of signals have already demonstrated to be useful to extract compact features to aid monitoring, classification, or detection. In this work we thus present the challenges and results of implementing the iterated coherent Hilbert decomposition (ICHD), a particularly effective algorithm to obtain an AM-FM decomposition, within a resource-constrained and low-power ARM Cortex-M4 microcontroller that is present in a wearable sensor we developed. We apply ICHD to the gyroscope data acquired from an inertial measurement unit (IMU) that is present in the sensor. Optimizing the implementation allowed us to achieve real-time performance using less then 16 % of the available CPU time, while consuming only about 5.4 mW of power, which results in a run-time of over 7 days using a small 250 mAh rechargeable cell
How to track behavioursâ changes toward more sustainable mobility habits: the serious game of TrafficO2
Social science and new communication technologies are influencing deeply the transportation domain and new models of urban sustainable development are constantly emerging as new possible solutions. One of the principal reasons is the capability to reach the motivations that foster us to choose one or other urban transportation systems. Itâs already feasible to develop solutions to aim the implementation and the rooting of more efficient and sustainable habits in urban transportation.
The present exploratory paper presents the activities conducted by innovation lab PUSH, with the Italian Ministry of Innovation and Research, for the development and the application of the social innovation project âtrafficO2â in the city of Palermo Italy. The project is a technology driven (a mobile app) serious game for sustainable urban mobility that tries to foster citizens, through information and tailor made incentives, to choose low emission means instead of using cars.
In a very controversial context given by the urban mobility of the city of Palermo, aim of the game is to build a strong and values driven self-organized community, able to change the actual urban condition without any command and control policies or any structural transformation.
The system was already tested twice with the community of students of the Palermo University; the paper shows the first results in terms of CO2 reduction and testers engagement
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
International audienceRecent years have witnessed the rapid development of human activity recognition (HAR) based on werable 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
Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers
In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x-, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, NaĂŻve Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition
Window Selection Impact in Human Activity Recognition
Signal segmentation is usually applied in the pre-processing step to make the data analysis easier. Windowing approach is commonly used for signal segmentation. However, it is unclear which type of window should be used to get optimum accuracy in human activity recognition. This study aimed to evaluat e which window type yields the optimum accuracy in human activity recognition. The acceleration data of walking, jogging, and running were collected from 20 young adults. Then, the recognition accuracy of each window types is evaluated and compared to determine the impact of window selection in human movement data. From the evaluation, the overlapping 75% window with 0.1 s length provides the highest accuracy with mean, standard deviation, maximum, minimum, and energy as the features. The result of this study could be used for future researches in relation to human activity recognition.