The hand tremor spectrum is modified by the inertial sensor mass during lightweight wearable and smartphone-based assessment in healthy young subjects

Wrist Function Test and Its Use to Assess Treatment Efficacy in Ischemic Stroke Survivors-A Pilot Study

Stroke is a major health issue causing high mortality and disability rates. Around 80% of stroke survivors experience impaired upper limb function, which is typically evaluated clinically. Functional electrical stimulation (FES) may help in motor function recovery. This study aimed to create an objective test of wrist flexion and extension functions and to assess the use of such a test in evaluating the efficacy of conventional rehabilitation therapy, alone and in combination with FES, in patients with a paretic upper limb. Background/Objectives: A total of 55 subjects were involved: 15 healthy volunteers and 40 post-stroke patients. The patients were split into two groups: one receiving only conventional rehabilitation (control group) and another receiving both conventional and FES therapies (FES group). Methods: Inertial sensors measured wrist flexion and extension parameters before and after the study treatment. Results: Both groups showed improvement based on the ARAT and FMA-UE scales. Normative values were established in the healthy group, revealing interhemispheric asymmetry. The wrist motion amplitudes and phases in both patient groups differed significantly from the healthy group. Initially, the paretic side had a 40-degree reduction compared to healthy subjects, while the non-paretic side showed a 10-17-degree decrease. After treatment, the FES group demonstrated a 4-10-degree increase in the wrist motion amplitude on the paretic side. The phase parameters did not change significantly in either group. Conclusions: The developed wrist flexion-extension test was shown to be objective and sensitive. The FES treatment improved the movement amplitude, although it did not alter the temporal structure of motion in both patient groups.

Technological interventions for the suppression of hand tremors: A literature review

A tremor is a neurological disorder that results in trembling or shaking in one or more body parts. A thorough literature review was conducted to investigate the methods for suppressing tremors. We looked for articles published between 1995 and 2024 in the databases CINAHL (Cumulative Index to Nursing and Allied Health Literature), PubMed, Medline, Embase, Scopus, and Cochrane. Two thousand two hundred fifty distinct items were discovered after an extensive search. Based only on the title, 250 were included. Two hundred papers were deemed ineligible after the abstracts were assessed. The remaining 26 articles were shortlisted after screening titles and abstracts and categorized based on treatment methods for hand tremors. According to the study's findings, deep brain stimulation (DBS) and electrical stimulation both reduced tremors considerably. It was also evident that attenuation systems and passive devices lessen the effects of tremors; target tracking tasks can lessen physiological tremors in postural posture; ET may have better hand functions after cold water treatment than warm water or at baseline; and targeted ultrasound thalamotomy is an effective treatment for ET, as it improved quality of life (QoL) significantly. Additionally, the design, development, and evaluation of wearable devices and pharmaceutical interventions for tremor suppression were investigated in detail. The main objective was to perform a comparative analysis of the merits and demerits of both treatment methodologies in terms of functional outcomes, users' comfort, and side effects. The review highlights wearable devices as a beneficial option for tremor suppression, offering comfort, safety, and advanced technology over pharmaceutical intervention methodologies.

Enhancing public health in developing nations through smartphone-based motor assessment

Several protocols for motor assessment have been validated for use on smartphones and could be employed by public healthcare systems to monitor motor functional losses in populations, particularly those with lower income levels. In addition to being cost-effective and widely distributed across populations of varying income levels, the use of smartphones in motor assessment offers a range of advantages that could be leveraged by governments, especially in developing and poorer countries. Some topics related to potential interventions should be considered by healthcare managers before initiating the implementation of such a digital intervention.

Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors

Background

Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge.

Methods

We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques.

Results

Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88.

Conclusion

We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Smartphone-based evaluation of static balance and mobility in long-lasting COVID-19 patients

Background

SARS-CoV-2 infection can lead to a variety of persistent sequelae, collectively known as long COVID-19. Deficits in postural balance have been reported in patients several months after COVID-19 infection. The purpose of this study was to evaluate the static balance and balance of individuals with long COVID-19 using inertial sensors in smartphones.

Methods

A total of 73 participants were included in this study, of which 41 had long COVID-19 and 32 served as controls. All participants in the long COVID-19 group reported physical complaints for at least 7 months after SARS-CoV-2 infection. Participants were evaluated using a built-in inertial sensor of a smartphone attached to the low back, which recorded inertial signals during a static balance and mobility task (timed up and go test). The parameters of static balance and mobility obtained from both groups were compared.

Results

The groups were matched for age and BMI. Of the 41 participants in the long COVID-19 group, 22 reported balance impairment and 33 had impaired balance in the Sharpened Romberg test. Static balance assessment revealed that the long COVID-19 group had greater postural instability with both eyes open and closed than the control group. In the TUG test, the long COVID-19 group showed greater acceleration during the sit-to-stand transition compared to the control group.

Conclusion

The smartphone was feasible to identify losses in the balance motor control and mobility of patients with long-lasting symptomatic COVID-19 even after several months or years. Attention to the balance impairment experienced by these patients could help prevent falls and improve their quality of life, and the use of the smartphone can expand this monitoring for a broader population.

Comparison of inertial records during anticipatory postural adjustments obtained with devices of different masses

Background

Step initiation involves anticipatory postural adjustments (APAs) that can be measured using inertial measurement units (IMUs) such as accelerometers. However, previous research has shown heterogeneity in terms of the population studied, sensors used, and methods employed. Validity against gold standard measurements was only found in some studies, and the weight of the sensors varied from 10 to 110 g. The weight of the device is a crucial factor to consider when assessing APAs, as APAs exhibit significantly lower magnitudes and are characterized by discrete oscillations in acceleration paths.

Objective

This study aims to validate the performance of a commercially available ultra-light sensor weighing only 5.6 g compared to a 168-g smartphone for measuring APAs during step initiation, using a video capture kinematics system as the gold standard. The hypothesis is that APA oscillation measurements obtained with the ultra-light sensor will exhibit greater similarity to those acquired using video capture than those obtained using a smartphone.

Materials and Methods

Twenty subjects were evaluated using a commercial lightweight MetaMotionC accelerometer, a smartphone and a system of cameras-kinematics with a reflective marker on lumbar vertebrae. The subjects initiated 10 trials of gait after a randomized command from the experimenter and APA variables were extracted: APAonset, APAamp, PEAKtime. A repeated measures ANOVA with post-hoc test analyzed the effect of device on APA measurements. Bland-Altman plots were used to evaluate agreement between MetaMotionC, smartphone, and kinematics measurements. Pearson's correlation coefficients were used to assess device correlation. Percentage error was calculated for each inertial sensor against kinematics. A paired Student's t-test compared th devices percentage error.

Results

The study found no significant difference in temporal variables APAonset and PEAKtime between MetaMotionC, smartphone, and kinematic instruments, but a significant difference for variable APAamp, with MetaMotionC yielding smaller measurements. The MetaMotionC had a near-perfect correlation with kinematic data in APAonset and APAamp, while the smartphone had a very large correlation in APAamp and a near-perfect correlation in APAonset and PEAKtime. Bland-Altman plots showed non-significant bias between smartphone and kinematics for all variables, while there was a significant bias between MetaMotionC and kinematics for APAamp. The percentage of relative error was not significantly different between the smartphone and MetaMotionC.

Conclusions

The temporal analysis can be assessed using ultralight sensors and smartphones, as MetaMotionC and smartphone-based measurements have been found to be valid compared to kinematics. However, caution should be exercised when using ultralight sensors for amplitude measurements, as additional research is necessary to determine their effectiveness in this regard.