The Rehapiano-Detecting, Measuring, and Analyzing Action Tremor Using Strain Gauges

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices

BACKGROUND

Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors.

METHODS

The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device".

RESULTS

Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy.

CONCLUSIONS

Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

Impact and trends in embedding field programmable gate arrays and microcontrollers in scientific instrumentation

Microcontrollers and field-programmable gate arrays have been largely leveraged in scientific instrumentation since decades. Recent advancements in the performance of these programmable digital devices, with hundreds of I/O pins, up to millions of logic cells, >10 Gb/s connectivity, and hundreds of MHz multiple clocks, have been accelerating this trend, extending the range of functions. The diversification of devices from very low-cost 8-bit microcontrollers up to 32-bit ARM-based ones and a system of chip combining programmable logic with processors make them ubiquitous in modern electronic systems, addressing diverse challenges from ultra-low power operation, with sub-µA quiescent current in sleep mode for portable and Internet of Things applications, to high-performance computing, such as in machine vision. In this Review, the main motivations (compactness, re-configurability, parallelization, low latency for sub-ns timing, and real-time control), the possible approaches of the adoption of embedded devices, and the achievable performances are discussed. Relevant examples of applications in opto-electronics, physics experiments, impedance, vibration, and temperature sensing from the recent literature are also reviewed. From this bird-eye view, key paradigms emerge, such as the blurring of boundaries between digital platforms and the pervasiveness of machine learning algorithms, significantly fostered by the possibility to be run in embedded devices for distributing intelligence in the environment.

Sensors, Signal and Image Processing in Biomedicine and Assisted Living

Sensor technologies are crucial in biomedicine, as the biomedical systems and devices used for screening and diagnosis rely on their efficiency and effectiveness [...].