Sabato Mellone

Validity of a Smartphone-based instrumented Timed Up and Go

The Timed Up and Go (TUG) is one of the most widely used clinical tests to assess balance and mobility. An instrumented Timed Up and Go (iTUG) makes use of a specialized measurement system (e.g. an accelerometer) to identify and evaluate specific mobility skills. Nowadays a Smartphone (SP) comes with a large set of embedded sensors, including an accelerometer. An SP is a user-friendly device able to perform ubiquitous sensing with a variety of connectivity options. In this study we evaluate the validity of an SP for instrumenting the TUG. We examined 49 subjects (59 ± 16 years old), without defining any inclusion criteria, using both an SP and a McRoberts Dynaport Hybrid, a device specifically designed for movement analysis. The statistical agreement between the two measurement systems is good for some of the parameters described in literature, which are of clear clinical value. Inter-rater reliability is often excellent and intra-rater reliability has been assessed in a subgroup of 25 subjects finding the same results for the two devices. In conclusion we found evidence that the SP is capable of becoming a pervasive and low-cost tool for the quantitative analysis of balance and mobility.

Continuous monitoring of turning in patients with movement disability.

Difficulty with turning is a major contributor to mobility disability and falls in people with movement disorders, such as Parkinsons disease (PD). Turning often results in freezing and/or falling in patients with PD. However, asking a patient to execute a turn in the clinic often does not reveal their impairments. Continuous monitoring of turning with wearable sensors during spontaneous daily activities may help clinicians and patients determine who is at risk of falls and could benefit from preventative interventions. In this study, we show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people. We developed an algorithm to detect and characterize turns during gait, using wearable inertial sensors. First, we validate the turning algorithm in the laboratory against a Motion Analysis system and against a video analysis of 21 PD patients and 19 control (CT) subjects wearing an inertial sensor on the pelvis. Compared to Motion Analysis and video, the algorithm maintained a sensitivity of 0.90 and 0.76 and a specificity of 0.75 and 0.65, respectively. Second, we apply the turning algorithm to data collected in the home from 12 PD and 18 CT subjects. The algorithm successfully detects turn characteristics, and the results show that, compared to controls, PD subjects tend to take shorter turns with smaller turn angles and more steps. Furthermore, PD subjects show more variability in all turn metrics throughout the day and the week.

Smartphone-based solutions for fall detection and prevention: the FARSEEING approach.

Falls are not an inevitable consequence of aging. The risk and rate of falls can be reduced. Recent improvements in smartphone technology enable implementation of a wide variety of services and applications, thus making the smartphone more of a digital companion than simply a communication tool. This paper presents the results obtained by the FARSEEING project where smartphones are one example of intervention in a population-based scenario. The applications developed take advantage of the smartphone-embedded inertial sensors and require that subjects wear the smartphone by means of a waist belt. The uFall Android application has been developed for monitoring the user’s motor activities at home. The application does not require any direct interaction with the user and it is also capable of running a real-time fall-detection algorithm. uTUG is a stand-alone application for instrumenting the Timed Up and Go test, which is a test often included in fall risk assessment protocols. The application acts like a pocket-sized motion laboratory, since it is capable not only of recording the trial but also of processing the data and immediately displaying the results. uTUG is designed to be self-administrable at home.ZusammenfassungStürze sind keine notwendige Folge des Alterns, sie können verhindert werden. Die jüngsten Entwicklungen der Smartphonetechnologie ermöglichen eine Vielzahl von Anwendungen und Applikationen, wodurch das Gerät nicht nur als Kommunikationswerkzeug, sondern zunehmend als digitaler Alltagsbegleiter dient. In diesem Artikel werden Ergebnisse des FARSEEING-Projekts präsentiert, bei dem Smartphones ein Interventionsbeispiel in einem populationsbezogenen Szenario sind. Die hier vorgestellten Applikationen nutzen die im Gerät integrierten Inertialsensoren. Das Smartphone wird dabei mit einem Hüftgurt getragen. Die uFall-Applikation dient zur innerhäuslichen Beobachtung der körperlichen Aktivität des Nutzers und ermöglicht eine algorithmusbasierte Echtzeitsturzerkennung. Die uTUG-Applikation instrumentalisiert den Timed-up-and-go(TUG)-Test (Zeit bis zum Aufstehen und Gehen), welcher häufig zur Messung des Sturzrisikos verwendet wird. Dieses „miniaturisierte Bewegungslabor“ erlaubt nicht nur die Durchführung einzelner Messungen, sondern stellt auch prozessierte Daten zur direkten Auswertung bereit. Die Applikationen ermöglichen die Eigenanwendung und erfordern keine direkte Interaktion des Nutzers mit dem Gerät.

Feature Selection for Accelerometer-Based Posture Analysis in Parkinson's Disease

Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinsons disease (PD), postural instability being one of PDs 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 diseases progression.

Quantification of Motor Impairment in Parkinson's Disease Using an Instrumented Timed Up and Go Test

The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinsons 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.

Hilbert–Huang-Based Tremor Removal to Assess Postural Properties From Accelerometers

Tremor is one of the symptoms of several disorders of the central and peripheral nervous system, such as Parkinsons disease (PD). The impairment of postural control is another symptom of PD. The conventional method of posture analysis uses force plates, but accelerometers can be a valid and reliable alternative. Both these measurement techniques are sensitive to tremor. Tremor affects postural measures and may thus lead to misleading results or interpretations. Linear low-pass filters (LPFs) are commonly employed for tremor removal. In this study, an alternative method, based on Hilbert-Huang transformation (HHT), is proposed. We examined 20 PD subjects, with and without tremor, and 20 control subjects. We compared the effectiveness of LPF and HHT-based filtering on a set of postural parameters extracted from acceleration signals. HHT has the advantage of providing a filter, which with no a priori knowledge, efficiently manages the nonlinear, nonstationary interference due to tremor, and beyond tremor, gives descriptive measures of postural function. Some of the differences found using LPF can instead be ascribed to inefficient noise/tremor suppression. Filter order and cutoff frequency are indeed critical when subjects exhibit a tremorous behavior, in which case LPF parameters should be chosen very carefully.

Dyskinesia detection and monitoring by a single sensor in patients with Parkinson's disease.

In current clinical practice, assessment of levodopa‐induced dyskinesias (LIDs) in Parkinsons disease (PD) is based on semiquantitative scales or patients’ diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist‐worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings.

Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors

Falls are by far the leading cause of fractures and accidents in the home environment. The current Cochrane reviews and other systematic reviews report on more than 200 intervention studies about fall prevention. A recent meta-analysis has summarized the most important risk factors of accidental falls. However, falls and fall-related injuries remain a major challenge. One novel approach to recognize, analyze, and work better toward preventing falls could be the differentiation of the fall event into separate phases. This might aid in reconsidering ways to design preventive efforts and diagnostic approaches. From a conceptual point of view, falls can be separated into a pre-fall phase, a falling phase, an impact phase, a resting phase, and a recovery phase. Patient and external observers are often unable to give detailed comments concerning these phases. With new technological developments, it is now at least partly possible to examine the phases of falls separately and to generate new hypotheses.The article describes the practicality and the limitations of this approach using body-fixed sensor technology. The features of the different phases are outlined with selected real-world fall signals.ZusammenfassungStürze sind die mit Abstand häufigsten Ursachen von Frakturen und häuslichen Verletzungen im Alter. In den Cochrane Reviews und anderen systematischen Analysen wurden mehr als 200 randomisierte Interventionsstudien zur Sturzprävention erfasst. Eine neue Metaanalyse liegt für die Risikofaktoren von Stürzen vor. Dennoch bleiben Stürze und sturzbedingte Verletzungen eine große Herausforderung. Ein neuer Ansatz zur Erkennung, Analyse und Prävention von Stürzen ist es, Stürze in Abschnitte aufzuteilen. Dies könnte bei der Erstellung diagnostischer und präventiver Ansätze helfen. Phänomenologisch ist offenkundig, dass es eine Vorphase, Fallphase, Aufprallphase, Ruhephase und mögliche Erholungsphase gibt. Patienten und Fremdbeobachter sind allerdings nicht in der Lage, hierzu exakte Angaben zu machen. Durch technologische Neuentwicklungen ist es nunmehr möglich, diese Abschnitte zumindest teilweise zu beurteilen und daraus erste Hypothesen abzuleiten.Der Artikel beschreibt dabei die Praktikabilität und Beschränkungen der Verwendung von am Körper getragenen Sensoren. Die Sturzphasen werden anhand von Fallbeispielen verdeutlicht.

The quality of turning in Parkinson’s disease: a compensatory strategy to prevent postural instability?

BackgroundThe ability to turn while walking is essential for daily living activities. Turning is slower and more steps are required to complete a turn in people with Parkinson’s disease (PD) compared to control subjects but it is unclear whether this altered strategy is pathological or compensatory. The aim of our study is to characterize the dynamics of postural stability during continuous series of turns while walking at various speeds in subjects with PD compared to control subjects. We hypothesize that people with PD slow their turns to compensate for impaired postural stability.MethodMotion analysis was used to compare gait kinematics between 12 subjects with PD in their ON state and 19 control subjects while walking continuously on a route composed of short, straight paths interspersed with eleven right and left turns between 30 and 180°. We asked subjects to perform the route at three different speeds: preferred, faster, and slower. Features describing gait spatio-temporal parameters and turning characteristics were extracted from marker trajectories. In addition, to quantify dynamic stability during turns we calculated the distance between the lateral edge of the base of support and the body center of mass, as well as the extrapolated body center of mass.ResultsSubjects with PD had slower turns and did not widen the distance between their feet for turning, compared to control subjects. Subjects with PD tended to cut short their turns compared to control subjects, resulting in a shorter walking path. Dynamic stability was smaller in the PD, compared to the healthy group, particularly for fast turning angles of 90°.ConclusionsThe slower turning speeds and larger turning angles in people with PD might reflect a compensatory strategy to prevent dynamic postural instability given their narrow base of support.

Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinsons 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 smartphones 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 correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinsons disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.