95 research outputs found
Centinela: A human activity recognition system based on acceleration and vital sign data
This paper presents Centinela, a system that combines acceleration data with vital signs to achieve highly accurate activity recognition. Centinela recognizes five activities: walking, running, sitting, ascending, and descending. The system includes a portable and unobtrusive real-time data collection platform, which only requires a single sensing device and a mobile phone. To extract features, both statistical and structural detectors are applied, and two new features are proposed to discriminate among activities during periods of vital sign stabilization. After evaluating eight different classifiers and three different time window sizes, our results show that Centinela achieves up to 95.7% overall accuracy, which is higher than current approaches under similar conditions. Our results also indicate that vital signs are useful to discriminate between certain activities. Indeed, Centinela achieves 100% accuracy for activities such as running and sitting, and slightly improves the classification accuracy for ascending compared to the cases that utilize acceleration data only
Human activity recognition with accelerometry: novel time and frequency features
Human Activity Recognition systems require objective and reliable methods that can
be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area.
This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition
methodology are introduced in this work, namely Log Scale Power Bandwidth and the
Markov Models application.
The Forward Feature Selection was adopted as the feature selection algorithm in order to
improve the clustering performances and limit the computational demands. This method
selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector.
Several Machine Learning algorithms were applied to the used accelerometry databases
– FCHA and PAMAP databases - and these showed promising results in activities recognition.
The developed algorithm set constitutes a mighty contribution for the development of
reliable evaluation methods of movement disorders for diagnosis and treatment applications
CONDITION MONITORING BASED ON A WIRELESS, DISTRIBUTED AND SCALABLE PLATFORM
Ph.DDOCTOR OF PHILOSOPH
A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data
International audienceThe rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements, physical activity and the environments in which they take place. And this can be done with high precision, thanks to miniature sensors integrated into our everyday devices. In this paper, we explore different methodologies for recognizing and characterizing physical activities performed by people wearing new smart devices. Whether it's smartglasses, smartwatches or smartphones, we show that each of these specialized wearables has a role to play in interpreting and monitoring moments in a user's life. In particular, we propose an approach that splits the concept of physical activity into two sub-categories that we call micro-and macro-activities. Micro-and macro-activities are supposed to have functional relationship with each other and should therefore help to better understand activities on a larger scale. Then, for each of these levels, we show different methods of collecting, interpreting and evaluating data from different sensor sources. Based on a sensing system we have developed using smart devices, we build two data sets before analyzing how to recognize such activities. Finally, we show different interactions and combinations between these scales and demonstrate that they have the potential to lead to new classes of applications, involving authentication or user profiling
Identification of Persons and Several Demographic Features based on Motion Analysis of Various Daily Activities using Wearable Sensors
In recent years, there has been an increasing interest in using the capabilities of wearable sensors, including
accelerometers, gyroscopes and magnetometers, to recognize individuals while undertaking a set of normal daily
activities. The past few years have seen considerable research exploring person recognition using wearable sensing
devices due to its significance in different applications, including security and human-computer interaction
applications.
This thesis explores the identification of subjects and related multiple biometric demographic attributes based on the
motion data of normal daily activities gathered using wearable sensor devices. First, it studies the recognition of 18
subjects based on motion data of 20 daily living activities using six wearable sensors affixed to different body
locations. Next, it investigates the task of classifying various biometric demographic features: age, gender, height,
and weight based on motion data of various activities gathered using two types of accelerometers and one gyroscope
wearable sensors. Initially, different significant parameters that impact the subjects' recognition success rates are
investigated. These include studying the performance of the three sensor sources: accelerometer, gyroscope, and
magnetometer, and the impact of their combinations. Furthermore, the impact of the number of different sensors
mounted at different body positions and the best body position to mount sensors are also studied. Next, the analysis
also explored which activities are more suitable for subject recognition, and lastly, the recognition success rates and
mutual confusion among individuals. In addition, the impact of several fundamental factors on the classification
performance of different demographic features using motion data collected from three sensors is studied. Those
factors include the performance evaluation of feature-set extracted from both time and frequency domains, feature
selection, individual sensor sources and multiple sources.
The key findings are: (I) Features extracted from all three sensor sources provide the highest accuracy of subjects
recognition. (2) The recognition accuracy is affected by the body position and the number of sensors. Ankle, chest,
and thigh positions outperform other positions in terms of the recognition accuracy of subjects. There is a
depreciating association between the subject classification accuracy and the number of sensors used. (3) Sedentary
activities such as watching tv, texting on the phone, writing with a pen, and using pc produce higher classification
results and distinguish persons efficiently due to the absence of motion noise in the signal. (4) Identifiability is not
uniformly distributed across subjects. (5) According to the classification results of considered biometric features,
both full and selected features-set derived from all three sources of two accelerometers and a gyroscope sensor
provide the highest classification accuracy of all biometric features compared to features derived from individual
sensors sources or pairs of sensors together. (6) Under all configurations and for all biometric features classified; the
time-domain features examined always outperformed the frequency domain features. Combining the two sets led to
no increase in classification accuracy over time-domain alone
Towards a smart fall detection system using wearable sensors
Empirical thesis."A thesis submitted as part of a cotutelle programme in partial fulfilment of Coventry University’s and Macquarie University’s requirements for the degree of Doctor of Philosophy" -- title page.Bibliography: pages 183-205.1. Introduction -- 2. Literature review -- 3. Falls and activities of daily living datasets -- 4. An analysis of fall-detection approaches -- 5. Event-triggered machine-learning approach (EvenT-ML) -- 6. Genetic-algorithm-based feature-selection technique for fall detection (GA-Fade) -- 7. Conclusions and future work -- References -- Appendices.A fall-detection system is employed in order to monitor an older person or infirm patient and alert their carer when a fall occurs. Some studies use wearable-sensor technologies to detect falls, as those technologies are getting smaller and cheaper. To date, wearable-sensor-based fall-detection approaches are categorised into threshold and machine-learning-based approaches. A high number of false alarms and a high computational cost are issues that are faced by the threshold- and machine-learning basedapproaches, respectively. The goal of this thesis is to address those issues by developing a novel low-computational-cost machine-learning-based approach for fall detection using accelerometer sensors.Toward this goal, existing fall-detection approaches (both threshold- and machine-learning-based) are explored and evaluated using publicly accessible datasets: Cogent, SisFall, and FARSEEING. Four machine-learning algorithms are implemented in this study: Classification and Regression Tree (CART), k-Nearest Neighbour (k-NN), Logistic Regression (LR), and Support Vector Machine (SVM). The experimental results show that using the correct size and type for the sliding window to segment the data stream can give the machine-learning-based approach a better detection rate than the threshold-based approach, though the difference between the threshold- and machine-learning-based approaches is not significant in some cases.To further improve the performance of the machine-learning-based approaches, fall stages (pre-impact, impact, and post-impact) are used as a basis for the feature extraction process. A novel approach called an event-triggered machine-learning approach for fall detection (EvenT-ML) is proposed, which can correctly align fall stages into a data segment and extract features based on those stages. Correctly aligning the stages to a data segment is difficult because of multiple high peaks, where a high peak usually indicates the impact stage, often occurring during the pre-impact stage. EvenT-ML significantly improves the detection rate and reduces the computational cost of existing machine-learning-based approaches, with an up to 97.6% F-score and a reduction in computational cost by a factor of up to 80 during feature extraction. Also, this technique can significantly outperform the threshold-based approach in all cases.Finally, to reduce the computational cost of EvenT-ML even further, the number of features needs to be reduced through a feature-selection process. A novel genetic-algorithm-based feature-selection technique (GA-Fade) is proposed, which uses multiple criteria to select features. GA-Fade considers the detection rate, the computational cost, and the number of sensors used as the selection criteria. GAFade is able to reduce the number of features by 60% on average, while achieving an F-score of up to 97.7%. The selected features also can give a significantly lower total computational cost than features that are selected by two single-criterion-based feature-selection techniques: SelectKBest and Recursive Feature Elimination.In summary, the techniques presented in this thesis significantly increase the detection rate of the machine-learning-based approach, so that a more reliable fall detection system can be achieved. Furthermore, as an additional advantage, these techniques can significantly reduce the computational cost of the machine-learning approach. This advantage indicates that the proposed machine-learning-based approach is more applicable to a small wearable device with limited resources (e.g., computing power and battery capacity) than the existing machine-learning-based approaches.Mode of access: World wide web1 online resource (xx, 211 pages) diagrams, graphs, table
Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring
Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements
Analysis of Android Device-Based Solutions for Fall Detection
Falls are a major cause of health and psychological problems as well as
hospitalization costs among older adults. Thus, the investigation on automatic Fall
Detection Systems (FDSs) has received special attention from the research community
during the last decade. In this area, the widespread popularity, decreasing price, computing
capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based
devices (especially smartphones) have fostered the adoption of this technology to deploy
wearable and inexpensive architectures for fall detection. This paper presents a critical and
thorough analysis of those existing fall detection systems that are based on Android devices.
The review systematically classifies and compares the proposals of the literature taking into
account different criteria such as the system architecture, the employed sensors, the detection
algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the
evaluation methods that are employed to assess the effectiveness of the detection process.
The review reveals the complete lack of a reference framework to validate and compare the
proposals. In addition, the study also shows that most research works do not evaluate the
actual applicability of the Android devices (with limited battery and computing resources) to
fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-
Usable Security for Wireless Body-Area Networks
We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors
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