Quantifying the importance of high frequency components on the amplitude of physiological tremor

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

Burst ranking for blind multi-image deblurring

We propose a new incremental aggregation algorithm for multi-image deblurring with automatic image selection. The primary motivation is that current burst deblurring methods do not handle well situations in which misalignment or out-of-context frames are present in the burst. These real-life situations result in poor reconstructions or manual selection of the images that are used to deblur. Automatically selecting the best frames within the burst to improve the base reconstruction is challenging because the number of possible images fusions is equal to the power set cardinal. Here, we approach the multi-image deblurring problem as a two steps process. First, we successfully learn a comparison function to rank a burst of images using a deep convolutional neural network. Then, an incremental Fourier burst accumulation with a reconstruction degradation mechanism is applied fusing only less blurred images that are sufficient to maximize the reconstruction quality. Experiments with the proposed algorithm have shown superior results when compared to other similar approaches, outperforming other methods described in the literature in previously described situations. We validate our findings on several synthetic and real datasets.

Removing Camera Shake via Weighted Fourier Burst Accumulation

Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method's rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently. Experiments with real camera data, and extensive comparisons, show that the proposed Fourier burst accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. Finally, we also present experiments in real high dynamic range (HDR) scenes, showing how the method can be straightforwardly extended to HDR photography.

Measuring tremor with a smartphone

Tremor is the most common movement disorder. However; characterizing it in large populations is not easily accomplished since current methodologies are not adapted to large-scale field studies. To overcome this challenge, a smartphone application was developed as a stand-alone platform to assess tremor. The current book chapter details the steps taken to validate this mobile application. Data recorded with the smartphone was analyzed online and offline as well as compared to laboratory equipment and a clinical scale. This allowed for the identification of the tremor properties that could reliably be characterized with the smartphone as well as the limits of the hardware. It also allowed for the identification of tasks that could be performed with the smartphone when tremor was being assessed. Finally, we confirmed the clinical relevance of the results provided by the smartphone application.

Using a smart phone as a standalone platform for detection and monitoring of pathological tremors

Introduction: Smart phones are becoming ubiquitous and their computing capabilities are ever increasing. Consequently, more attention is geared toward their potential use in research and medical settings. For instance, their built-in hardware can provide quantitative data for different movements. Therefore, the goal of the current study was to evaluate the capabilities of a standalone smart phone platform to characterize tremor. Results: Algorithms for tremor recording and online analysis can be implemented within a smart phone. The smart phone provides reliable time- and frequency-domain tremor characteristics. The smart phone can also provide medically relevant tremor assessments. Discussion: Smart phones have the potential to provide researchers and clinicians with quantitative short- and long-term tremor assessments that are currently not easily available. Methods: A smart phone application for tremor quantification and online analysis was developed. Then, smart phone results were compared to those obtained simultaneously with a laboratory accelerometer. Finally, results from the smart phone were compared to clinical tremor assessments.

Upper limb position control in fibromyalgia

BACKGROUND

Motor problems are reported by patients with fibromyalgia (FM). However, the mechanisms leading to alterations in motor performance are not well understood. In this study, upper limb position control during sustained isometric contractions was investigated in patients with FM and in healthy controls (HCs).

METHODS

Fifteen female FM patients and 13 HCs were asked to keep a constant upper limb position during sustained elbow flexion and shoulder abduction, respectively. Subjects received real-time visual feedback on limb position and both tasks were performed unloaded and while supporting loads (1, 2, and 3 kg). Accelerations of the dominant upper limb were recorded, with variance (SD of mean position) and power spectrum analysis used to characterize limb position control. Normalized power of the acceleration signal was extracted for three frequency bands: 1-3 Hz, 4-7 Hz, and 8-12 Hz.

RESULTS

Variance increased with load in both tasks (P < 0.001) but did not differ significantly between patients and HCs (P > 0.17). Power spectrum analysis showed that the FM patients had a higher proportion of normalized power in the 1-3 Hz band, and a lower proportion of normalized power in the 8-12 Hz band compared to HCs (P < 0.05). The results were consistent for all load conditions and for both elbow flexion and shoulder abduction.

CONCLUSION

FM patients exhibit an altered neuromuscular strategy for upper limb position control compared to HCs. The predominance of low-frequency limb oscillations among FM patients may indicate a sensory deficit.

The effect of changes in joint angle on the characteristics of physiological tremor

INTRODUCTION

Physiological tremor, as a whole, can be influenced by changes in muscle activity. However, the origin of low-frequency physiological tremor oscillations has yet to be conclusively determined. It is possible that by experimentally manipulating muscular activity, a better determination of the origin of those low-frequency oscillations can be achieved. It was demonstrated that changes in joint angle modify characteristics of muscular activity. As such, we hypothesize that changes in wrist-joint angle will alter the characteristics of low-frequency physiological tremor oscillations.

OBJECTIVE

Assess the influence of changes in joint angle of the wrist on characteristics of physiological finger tremor.

METHODS

Physiological finger tremor was recorded (n = 25) using a laser displacement system while the arm and hand were supported. The relative angle between the dorsum of the hand and the forearm was altered between conditions (135°, 180°, 225° and 270°), while the hand and the finger remained parallel to the ground. EMG of the extensors and flexors were also recorded.

RESULTS

Tremor amplitude was significantly altered by changes in wrist-joint angle. This was especially the case for lower frequency oscillations. In addition, electromyography properties of forearm muscles were also significantly modified by changes in wrist-joint angles.

CONCLUSIONS

This study demonstrates that changes in wrist-joint angle modify the characteristics of physiological finger tremor. This should be taken into account when interpreting tremor data as well as when developing tools to minimize tremor.

Changes in physiological tremor associated with an epileptic seizure: a case report

INTRODUCTION

Epileptic seizures are associated with motor, sensory, somatosensory or autonomic symptoms that have all been described in varying detail over the years. Of interest in the present report is a case of normal physiological tremor, which to date has never been evaluated prior to and during an epileptic seizure. In fact, there is only anecdotal mention of pre-ictal and ictal changes in clinically noticeable tremor in the literature.

CASE PRESENTATION

Our patient was a left-handed, 27-year-old Caucasian woman diagnosed seven years previously with partial epileptic seizures, secondarily generalized. Physiological tremor was measured simultaneously on the index finger of both hands of our patient. Electromyography as well as heart rate and respiration were also monitored. A previously performed electroencephalography examination revealed abnormal oscillations focalized to the left primary somatosensory cortex. She was also diagnosed with left frontal neuronal heterotopias. We detected subclinical changes in tremor characteristics, such as amplitude, median power frequency and power dispersion, contralateral to the localization of epileptic activity. Tremor characteristics remained relatively steady ipsilateral to the localization of the epileptic activity.

CONCLUSIONS

Changes in physiological tremor characteristics should be considered as another possible pre-ictal or ictal manifestation. We propose that the network associated with physiological tremor might be more sensitive to abnormal oscillations generated within the central nervous system by epileptic activity from certain structures.