S. Mellone - Academia.edu (original) (raw)
Papers by S. Mellone
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
European Geriatric Medicine, 2013
European Geriatric Medicine, 2014
Lecture Notes in Computer Science, 2013
2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011, 2011
Abstract The aim of this study is to develop a system for investigating human falls and mobility ... more Abstract The aim of this study is to develop a system for investigating human falls and mobility based on a Smartphone platform. We have designed and tested a set of software applications building on the inertial data captured from the tri-axial accelerometer sensor embedded in the Smartphone. We will describe here two applications: a fall detection and management application, and an application for the administration of a popular and standardized test in the field of human mobility assessment, namely the Timed-Up-and-Go ...
Communications in Computer and Information Science, 2013
ABSTRACT Evaluation of the locomotor function is important for several clinical applications (e.g... more ABSTRACT Evaluation of the locomotor function is important for several clinical applications (e.g. fall risk of the elderly, characterization of a disease with motor complications). We consider the Timed Up and Go test which is widely used to evaluate the locomotor function in Parkinson’s Disease (PD). Twenty PD and twenty age-matched control subjects performed an instrumented version of the test, where wearable accelerometers were used to gather quantitative information. Several measures were extracted from the acceleration signals; the aim is to find, by means of a feature selection, the best set that can discriminate between healthy and PD subjects. A wrapper feature selection was implemented with an exhaustive search for subsets from 1 to 3 features. A nested leave-one-out cross validation (LOOCV) was implemented, to limit a possible selection bias. With the selected features a good accuracy is obtained (7.5% of misclassification rate) in the classification between PD and healthy subjects.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2011
The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's d... more The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlat...
Zeitschrift für Gerontologie und Geriatrie, 2013
Parkinsonism & Related Disorders, 2010
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson&amp... more The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.
IEEE Transactions on Information Technology in Biomedicine, 2000
Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson&... more Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson's disease (PD), postural instability being one of PD's major symptoms. The aim of this study was to assess the feasibility of using accelerometers to characterize the postural behavior of early mild PD subjects. Twenty PD and 20 control subjects, wearing an accelerometer on the lower back, were tested in five conditions characterized by sensory and attentional perturbation. A total of 175 measures were computed from the signals to quantify tremor, acceleration, and displacement of body sway. Feature selection was implemented to identify the subsets of measures that better characterize the distinctive behavior of PD and control subjects. It was based on different classifiers and on a nested cross validation, to maximize robustness of selection with respect to changes in the training set. Several subsets of three features achieved misclassification rates as low as 5%. Many of them included a tremor-related measure, a postural measure in the frequency domain, and a postural displacement measure. Results suggest that quantitative posture analysis using a single accelerometer and a simple test protocol may provide useful information to characterize early PD subjects. This protocol is potentially usable to monitor the disease's progression.
IEEE Transactions on Biomedical Engineering, 2000
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
European Geriatric Medicine, 2013
European Geriatric Medicine, 2014
Lecture Notes in Computer Science, 2013
2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011, 2011
Abstract The aim of this study is to develop a system for investigating human falls and mobility ... more Abstract The aim of this study is to develop a system for investigating human falls and mobility based on a Smartphone platform. We have designed and tested a set of software applications building on the inertial data captured from the tri-axial accelerometer sensor embedded in the Smartphone. We will describe here two applications: a fall detection and management application, and an application for the administration of a popular and standardized test in the field of human mobility assessment, namely the Timed-Up-and-Go ...
Communications in Computer and Information Science, 2013
ABSTRACT Evaluation of the locomotor function is important for several clinical applications (e.g... more ABSTRACT Evaluation of the locomotor function is important for several clinical applications (e.g. fall risk of the elderly, characterization of a disease with motor complications). We consider the Timed Up and Go test which is widely used to evaluate the locomotor function in Parkinson’s Disease (PD). Twenty PD and twenty age-matched control subjects performed an instrumented version of the test, where wearable accelerometers were used to gather quantitative information. Several measures were extracted from the acceleration signals; the aim is to find, by means of a feature selection, the best set that can discriminate between healthy and PD subjects. A wrapper feature selection was implemented with an exhaustive search for subsets from 1 to 3 features. A nested leave-one-out cross validation (LOOCV) was implemented, to limit a possible selection bias. With the selected features a good accuracy is obtained (7.5% of misclassification rate) in the classification between PD and healthy subjects.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2011
The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's d... more The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlat...
Zeitschrift für Gerontologie und Geriatrie, 2013
Parkinsonism & Related Disorders, 2010
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson&amp... more The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.
IEEE Transactions on Information Technology in Biomedicine, 2000
Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson&... more Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson's disease (PD), postural instability being one of PD's major symptoms. The aim of this study was to assess the feasibility of using accelerometers to characterize the postural behavior of early mild PD subjects. Twenty PD and 20 control subjects, wearing an accelerometer on the lower back, were tested in five conditions characterized by sensory and attentional perturbation. A total of 175 measures were computed from the signals to quantify tremor, acceleration, and displacement of body sway. Feature selection was implemented to identify the subsets of measures that better characterize the distinctive behavior of PD and control subjects. It was based on different classifiers and on a nested cross validation, to maximize robustness of selection with respect to changes in the training set. Several subsets of three features achieved misclassification rates as low as 5%. Many of them included a tremor-related measure, a postural measure in the frequency domain, and a postural displacement measure. Results suggest that quantitative posture analysis using a single accelerometer and a simple test protocol may provide useful information to characterize early PD subjects. This protocol is potentially usable to monitor the disease's progression.
IEEE Transactions on Biomedical Engineering, 2000