1,721,159 research outputs found
Generalized linear spectral models
No full textIn this chapter we consider a class of parametric spectrum esti- mators based on a generalized linear model for exponential random variables with power link. The power transformation of the spectrum of a stationary process can be expanded in a Fourier series, with the coefficients representing generalised autocovariances. Direct Whittle estimation of the coefficients is generally unfeasible, as they are subject to constraints (the autocovariances need to be a positive semidefinite sequence). The problem can be overcome by using an ARMA repre- sentation for the power transformation of the spectrum. Estimation is carried out by maximising the Whittle likelihood, whereas the se- lection of a spectral model, as a function of the power transformation parameter and the ARMA orders, can be carried out by information criteria. The proposed methods are applied to the estimation of the inverse autocorrelation function and the related problem of selecting the optimal interpolator, and for the identification of spectral peaks. More generally, they can be applied to spectral estimation with pos- sibly misspecified models
Wearable Technologies for Monitoring Upper Extremity Functions During Daily Life in Neurologically Impaired Individuals
Full text linkNeurological disorders, including stroke, spinal cord injuries, multiple sclerosis, and Parkinson\u27s disease, generally lead to diminished upper extremity (UE) function, impacting individuals\u27 independence and quality of life. Traditional assessments predominantly focus on standardized clinical tasks, offering limited insights into real-life UE performance. In this context, this review focuses on wearable technologies as a promising solution to monitor UE function in neurologically impaired individuals during daily life activities. Our primary objective is to categorize the different sensors, review the data collection and understand the employed data processing approaches. After screening over 1500 papers and including 21 studies, what comes to light is that the majority of them involved stroke survivors, and predominantly employed accelerometers or inertial measurement units to collect kinematics. Most analyses in these studies were performed offline, focusing on activity duration and frequency as key metrics. Although wearable technology shows potential in monitoring UE function in real-life scenarios, it also appears that a solution combining non-intrusiveness, lightweight design, detailed hand and finger movement capture, contextual information, extended recording duration, ease of use, and privacy protection remains an elusive goal. These are critical characteristics for a monitoring solution and researchers in the field should try to integrate the most in future developments. Last but not least, it stands out a growing necessity for a multimodal approach in capturing comprehensive data on UE function during real-life activities to enhance the personalization of rehabilitation strategies and ultimately improve outcomes for these individuals.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 202
Generalised Linear Cepstral Models for the Spectrum of a Time Series
Full text linkThe paper introduces the class of generalised linear models with Box-Cox link for the spectrum of a time series. The Box-Cox transformation of the spectral density is represented as a finite Fourier polynomial, with coefficients, that we term generalised cepstral coefficients, providing a complete characterisation of the properties of the random process. The link function depends on a power transformation parameter and encompasses the exponential model (logarithmic link), the autoregressive model (inverse link), and the moving average model (identity link). One of the merits of this model class is the possibility of nesting alternative spectral estimation methods under the same likelihood-based framework, so that the selection of a particular parametric spectrum amounts to estimating the transformation parameter. We also show that the generalised cepstral coefficients are a one to one function of the inverse partial autocorrelations of the process, which can be used to evaluate the mutual information between the past and the future of the process
Generalized Linear Spectral Models for Locally Stationary Processes
No full textA class of parametric models for locally stationary processes is introduced. The class depends on a power parameter that applies to the time-varying spectrum so that it can be locally represented by a (finite low dimensional) Fourier polynomial. The coefficients of the polynomial have an interpretation as time-varying autocovariances, whose dynamics are determined by a linear combination of smooth transition functions, depending on some static parameters. Frequency domain estimation is based on the generalized Whittle likelihood and the pre-periodogram, while model selection is performed through information criteria. Change points are identified via a sequence of score tests. Consistency and asymptotic normality are proved for the parametric estimators considered in the paper, under weak assumptions on the time-varying parameters
Generalised partial autocorrelations and the mutual information between past and future
No full textThe paper introduces the generalised partial autocorrelation (GPAC) coefficients of a stationary stochastic process. The latter are related to the generalised autocovariances, the inverse Fourier transform coefficients of a power transformation of the spectral density function. By interpreting the generalised partial autocorrelations as the partial autocorrelation coefficients of an auxiliary process, we derive their properties and relate them to essential features of the original process.
Based on a parameterisation suggested by Barndorff-Nielsen and Schou (1973) and on Whittle likelihood, we develop an estimation strategy for the GPAC coefficients. We further prove that the GPAC coefficients can be used to estimate the mutual information between the past and the future of a time series
Assessment of wearable robotics performance in patients with neurological conditions
Full text linkPurpose of reviewWhile wearable robotics is expanding within clinical settings, particularly for neurological rehabilitation, there is still a lack of consensus on how to effectively assess the performance of these devices. This review focuses on the most common metrics, whose selection and design are crucial for optimizing treatment outcomes and potentially improve the standard care.Recent findingsThe literature reveals that while wearable robots are equipped with various embedded sensors, most studies still rely on traditional, nontechnological methods for assessment. Recent studies have shown that, although quantitative data from embedded sensors are available (e.g., kinematics), these are underutilized in favor of qualitative assessments. A trend toward integrating automatic assessments from the devices themselves is emerging, with a few notable studies pioneering this approach.SummaryOur analysis suggests a critical need for developing standardized metrics that leverage the data from embedded sensors in wearable robots. This shift could enhance the accuracy of patient assessments and the effectiveness of rehabilitation strategies, ultimately leading to better patient outcomes in neurological rehabilitation.TN
Feasibility of a Portable, Wearable, High-Density Surface EMG Device for Detecting Functional Hand-Object Interactions
No full textAir Efficient Soft Wearable Robot for High-Torque Elbow Flexion Assistance
No full text: Recent developments in soft wearable robots have shown promise for assistive and rehabilitative use-cases. For inflatable approaches, a major challenge in developing portable systems is finding a balance between portability, performance, and usability. In this paper, we present a textile-based robotic sleeve that can provide functional elbow flexion assistance and is compatible with a portable actuation unit (PAU). Flexion is driven by a curved textile actuator with internal pneumatic supports (IPS). We show that the addition of IPS improves torque generation and increases battery-powered actuations by 60%. We demonstrate that the device can provide enough torque throughout the ROM of the elbow joint for daily life assistance. Specifically, the device generates 13.5 Nm of torque at 90°. Experimental testing in five healthy individuals and two individuals with Amyotrophic Lateral Sclerosis (ALS) demonstrates its impact on wearer muscle activity and kinematics. The results with healthy subjects show that the device was able to reduce the bicep muscle activity by an average of 49.1±13.3% during static and dynamic exercises, 43.6±11.1% during simulated ADLs, and provided an assisted ROM of 134°±13°. Both ALS participants reported a reduced rate of perceived exertion during both static and dynamic tasks while wearing the device and had an average ROM of 115°±8°. Future work will explore other applications of the IPS and extend the approach to assisting multiple joints
An Ultrathin and Lightweight Soft Inflatable Actuator for Natural Tactile Sensory Feedback
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