112,149 research outputs found
Towards a fuzzy-based multi-classifier selection module for activity recognition applications
Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements
Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information
Automatic detection of public transport (PT) usage has important applications
for intelligent transport systems. It is crucial for understanding the
commuting habits of passengers at large and over longer periods of time. It
also enables compilation of door-to-door trip chains, which in turn can assist
public transport providers in improved optimisation of their transport
networks. In addition, predictions of future trips based on past activities can
be used to assist passengers with targeted information. This article documents
a dataset compiled from a day of active commuting by a small group of people
using different means of PT in the Helsinki region. Mobility data was collected
by two means: (a) manually written details of each PT trip during the day, and
(b) measurements using sensors of travellers' mobile devices. The manual log is
used to cross-check and verify the results derived from automatic measurements.
The mobile client application used for our data collection provides a fully
automated measurement service and implements a set of algorithms for decreasing
battery consumption. The live locations of some of the public transport
vehicles in the region were made available by the local transport provider and
sampled with a 30-second interval. The stopping times of local trains at
stations during the day were retrieved from the railway operator. The static
timetable information of all the PT vehicles operating in the area is made
available by the transport provider, and linked to our dataset. The challenge
is to correctly detect as many manually logged trips as possible by using the
automatically collected data. This paper includes an analysis of challenges due
to missing or partially sampled information in the data, and initial results
from automatic recognition using a set of algorithms. Improvement of correct
recognitions is left as an ongoing challenge.Comment: 22 pages, 7 figures, 10 table
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition
Motion sensors integrated into wearable and mobile devices provide valuable
information about the device users. Machine learning and, recently, deep
learning techniques have been used to characterize sensor data. Mostly, a
single task, such as recognition of activities, is targeted, and the data is
processed centrally at a server or in a cloud environment. However, the same
sensor data can be utilized for multiple tasks and distributed machine-learning
techniques can be used without the requirement of the transmission of data to a
centre. This paper explores Federated Transfer Learning in a Multi-Task manner
for both sensor-based human activity recognition and device position
identification tasks. The OpenHAR framework is used to train the models, which
contains ten smaller datasets. The aim is to obtain model(s) applicable for
both tasks in different datasets, which may include only some label types.
Multiple experiments are carried in the Flower federated learning environment
using the DeepConvLSTM architecture. Results are presented for federated and
centralized versions under different parameters and restrictions. By utilizing
transfer learning and training a task-specific and personalized federated
model, we obtained a similar accuracy with training each client individually
and higher accuracy than a fully centralized approach.Comment: Subimtted to Asian Conference in Machine Learning (ACML) 2023,
Pattern Recognition in Health Analysis Workshop, 7 pages, 3 figure
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
BodySpace: inferring body pose for natural control of a music player
We describe the BodySpace system, which uses inertial sensing and pattern recognition to allow the gestural control of a music player by placing the device at different parts of the body. We demonstrate a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based techniques can shape gestural interaction
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