Assessment of Aphasia Across the International Classification of Functioning, Disability and Health Using an iPad-Based Application.

BACKGROUND: Access2Aphasia™ is an iPad™-based aphasia assessment application that enables real-time audiovisual communication between people with aphasia (PWA) and speech-language pathologists (SLPs), and the use of supported conversation techniques. This study aimed to establish the reliability of aphasia assessment across the International Classification of Functioning, Disability and Health (ICF) using Access2Aphasia, and compare it with face-to-face (FTF) assessment. Consumer perspectives of Access2Aphasia were also examined. MATERIALS AND METHODS: Thirty PWA were randomized into two conditions: online-led and FTF assessment. Participants in the online-led group were assessed remotely using Access2Aphasia™ in their own homes, while an FTF SLP scored silently simultaneously. Participants in the FTF group were assessed FTF using standard administration materials. Assessment included two subtests of the Psycholinguistic Assessment of Language Processing Activities (PALPA) and the Assessment of Living with Aphasia (ALA) to allow for outcomes to be captured across the ICF domains. Consumer perspectives on Access2Aphasia were obtained from both PWA and research SLPs in the online-led group. RESULTS: Kappa statistics indicated moderate to almost perfect agreement between online and FTF SLPs (k = 0.71-1.00). Intrarater and interrater reliability was excellent (ICC = 0.99-1.00) and equivalent for the online-led and FTF conditions. Both PWA and research SLPs in the online-led group reported being satisfied with the experience overall, with suggestions provided by research SLPs to improve Access2Aphasia. CONCLUSION: This study supports the provision of iPad-based aphasia assessments across the ICF in the online environment, with comparable reliability to FTF assessments. Future research is warranted to support the development of iPad-based aphasia assessment and treatment as an alternative mode of service delivery to PWA.

Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) trial: protocol of a randomized clinical trial on tele-rehabilitation for stroke patients.

BACKGROUND: Most acute stroke patients with disabilities do not receive recommended rehabilitation following discharge to the community. Functional and social barriers are common reasons for non-adherence to post-discharge rehabilitation. Home rehabilitation is an alternative to centre-based rehabilitation but is costlier. Tele-rehabilitation is a possible solution, allowing for remote supervision of rehabilitation and eliminating access barriers. The objective of the Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) trial is to determine if a novel tele-rehabilitation intervention for the first three months after stroke admission improves functional recovery compared to usual care. METHODS/DESIGN: This is a single blind (evaluator blinded), parallel, two-arm randomised controlled trial study design involving 100 recent stroke patients. The inclusion criteria are age ≥40 years, having caregiver support and recent stroke defined as stroke diagnosis within 4 weeks. Consenting participants will be randomized with varying block size of 4 or 6 assuming a 1:1 treatment allocation with the participating centre as the stratification factor. The baseline assessment will be done within 4 weeks of stroke onset, followed by follow-up assessments at 3 and 6 months. The tele-rehabilitation intervention lasts for 3 months and includes exercise 5-days-a-week using an iPad-based system that allows recording of daily exercise with video and sensor data and weekly video-conferencing with tele-therapists after data review. Those allocated to the control group will receive usual care. The primary outcome measure is improvement in life task's social activity participation at three months as measured by the disability component of the Jette Late Life Functional and Disability Instrument (LLFDI). Secondary outcome variables consist of gait speed (Timed 5-Meter Walk Test) and endurance (Two-Minute Walk test), performance of basic activities of daily living (Shah-modified Barthel Index), balance confidence (Activities-Specific Balance Confidence Scale), patient self-reported health-related quality-of-life [Euro-QOL (EQ-5D)], health service utilization (Singapore Stroke Study Health Service Utilization Form) and caregiver reported stress (Zarit Caregiver Burden Inventory). DISCUSSION: The goal of this trial is to provide evidence on the potential benefit and cost-effectiveness of this novel tele-rehabilitation programme which will guide health care decision-making and potentially improve performance of post-stroke community-based rehabilitation. TRIAL REGISTRATION: This trial protocol was registered under ClinicalTrials.gov on 18 July 2013 as study title "The Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) Study" (ID: The STARS Study, ClinicalTrials.gov Identifier: NCT01905917 ).

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS: This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Detection of Freezing of Gait Using Convolutional Neural Networks and Data From Lower Limb Motion Sensors.

Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.

Home-based tele-rehabilitation presents comparable positive impact on self-reported functional outcomes as usual care: The Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) randomised controlled trial.

INTRODUCTION: The aim of this research was to evaluate the impact of a novel tele-rehabilitation system on self-reported functional outcomes compared to usual care during the first three months after stroke. METHODS: A parallel, two-arm, evaluator-blinded, randomised controlled trial was conducted. Adults aged ≥40 years who had suffered a stroke within four weeks of the start of the study were recruited from the general community. The intervention group received access to a novel tele-rehabilitation system and programme for three months. The primary outcome measures utilised were the frequency and limitation total scores of the Late-Life Function and Disability Instrument (LLFDI) at three months. RESULTS: A total of 124 individuals were recruited. The mean differences in the LLDFI frequency and limitation total scores at three months comparing the intervention and control groups were -3.30 (95% confidence interval (CI) -7.81 to 1.21) and -6.90 (95% CI -15.02 to 1.22), respectively. Adjusting for the respective baseline covariates and baseline Barthel Index also showed no significant difference between interventions in the LLFDI outcomes. DISCUSSION: The intervention and control groups self-reported similar improvements in functional outcomes. Tele-rehabilitation may be a viable option to provide post-stroke rehabilitation services in Singapore while reducing barriers to continue rehabilitation conventionally after discharge from hospital and encouraging more participation.

Convolutional Neural Network for Freezing of Gait Detection Leveraging the Continuous Wavelet Transform on Lower Extremities Wearable Sensors Data.

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.

Rapid Geriatric Assessment Using Mobile App in Primary Care: Prevalence of Geriatric Syndromes and Review of Its Feasibility.

With the aging population and consequent increase in associated prevalence of frailty, dementia, and multimorbidity, primary care physicians will be overwhelmed with the complexity of the psychosocial and clinical presentation. Geriatric syndromes including frailty, sarcopenia, cognitive impairment, and anorexia of aging (AA) either in isolation or in combination are associated with an increased risk of adverse outcomes and if recognized early, and appropriately managed, will lead to decreased disability. Primary care practices are often located in residential settings and are in an ideal position to incorporate preventive screening and geriatric assessment with personalized management. However, primary care physicians lack the time, multidisciplinary resources, or skills to conduct geriatric assessment, and the limited number of geriatricians worldwide further complicates the matter. There is no one effective strategy to implement geriatric assessment in primary care which is rapid, cost-effective, and do not require geriatricians. Rapid Geriatric Assessment (RGA) takes <5 min to complete. It screens for frailty, sarcopenia, AA, and cognition with assisted management pathway without the need of a geriatrician. We developed RGA iPad application for screening with assisted management in two primary care practices and explored the feasibility and overall prevalence of frailty, sarcopenia, and AA. The assessment was conducted by trained nurses and coordinators. Among 2,589 older patients ≥65 years old, the prevalence of frailty was 5.9%, pre-frail 31.2%, and robust 62.9%. Fatigue was present in 17.8%, and among them, the prevalence of undiagnosed depression as assessed by the Patient Health Questionnaire (PHQ)-9 was 76.4% and 13.5% of total. The prevalence of sarcopenia was 15.4%, and 13.9% experienced at least one fall in the past year. AA was prevalent in 10.9%. The time taken to do the assessment with defined algorithm was on average 5 min or less per patient, and 96% managed to complete the assessment prior to seeing their doctor in the same session. The RGA app is a rapid and feasible tool to be used by any healthcare professional in primary care for identification of geriatric syndrome with assisted management.

A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease.

Freezing of Gait (FoG) is a common motor-related impairment among Parkinson's disease patients, which substantially reduces their quality of life and puts them at risk of falls. These patients benefit from wearable FoG detection systems that provide timely biofeedback cues and hence help them regain control over their gait. Unfortunately, the systems proposed thus far are bulky and obtrusive when worn. The objective of this paper is to demonstrate the first integration of an FoG detection system into a single sensor node. To achieve such an integration, features with low computational load are selected and dedicated hardware is designed that limits area and memory utilization. Classification is achieved with a neural network that is capable of learning in real time and thus allows the system to adapt to a patient during run-time. A small form factor FPGA implements the feature extraction and classification, whereas a custom PCB integrates the system into a single node. The system fits into a 4.5 × 3.5 × 1.5 cm 3 housing case, weighs 32 g, and achieves 95.6% sensitivity and 90.2% specificity when adapted to a patient. Biofeedback cues are provided either through auditory or somatosensory means and the system can remain operational for longer than 9 h while providing cues. The proposed system is highly competitive in terms of classification performance and excels with respect to wearability and real-time patient adaptivity.

Direct measurement of beam size in a spectroscopic ellipsometry setup.

Spectroscopic ellipsometry signals used in thin film analysis are dependent on the beam probe size. In this work, we report a technique to determine the beam size that uses the existing detection facilities in a spectroscopic ellipsometry setup without the need to rearrange the optical components. The intensity signal recorded with the technique comprises a coupled boundary diffraction and knife edge wave that can be isolated using nonlinear fitting. This then permitted an accurate measurement of the beam size with the stronger knife edge component. The technique has the added advantage of picking up chromatic aberration in the probing lens which may be a factor in ellipsometry measurement.

Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values.

BACKGROUND: Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. METHODS: 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. RESULTS: Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. CONCLUSION: Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.

A novel constrained H2 optimization algorithm for mechatronics design in flexure-linked biaxial gantry.

The biaxial gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers from breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a flexure-linked biaxial gantry is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end-effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-to-point reference trajectories with same dynamics but different steps. From here, an H2 optimization problem with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal step. Comparative experimental results obtained on the testbed are presented to verify the effectiveness of the proposed method.

Wireless wearable range-of-motion sensor system for upper and lower extremity joints: a validation study.

Range-of-motion (ROM) assessment is a critical assessment tool during the rehabilitation process. The conventional approach uses the goniometer which remains the most reliable instrument but it is usually time-consuming and subject to both intra- and inter-therapist measurement errors. An automated wireless wearable sensor system for the measurement of ROM has previously been developed by the current authors. Presented is the correlation and accuracy of the automated wireless wearable sensor system against a goniometer in measuring ROM in the major joints of upper (UEs) and lower extremities (LEs) in 19 healthy subjects and 20 newly disabled inpatients through intra (same) subject comparison of ROM assessments between the sensor system against goniometer measurements by physical therapists. In healthy subjects, ROM measurements using the new sensor system were highly correlated with goniometry, with 95% of differences < 20° and 10° for most movements in major joints of UE and LE, respectively. Among inpatients undergoing rehabilitation, ROM measurements using the new sensor system were also highly correlated with goniometry, with 95% of the differences being < 20° and 25° for most movements in the major joints of UE and LE, respectively.

Note: real time three-dimensional topography measurement of microfluidic devices with pillar structures using confocal microscope.

Miniature pillars are three-dimensional (3D) features commonly found in microfluidic device. These features are usually employed as filters. Non-confocal profilometers have difficulties in measuring 3D topography of pillar structures in transparent microfluidic devices. Confocal sensors can be used to measure the 3D topography of pillar structures but they are usually time consuming due to the scanning process. We have developed a technique to measure 3D topography using a modified confocal microscope with a spinning Nipkow disk and chromatic confocal technique. Experimental results on a microfluidic device with pillar structures demonstrate the feasibility of the proposed technique. Our technique is suitable for in situ, real time measurement of microfluidic device at production speed since it requires only one confocal image to complete a measurement.

Note: development of high speed confocal 3D profilometer.

A high-speed confocal 3D profilometer based on the chromatic confocal technology and spinning Nipkow disk technique has been developed and tested. It can measure a whole surface topography by taking only one image that requires less than 0.3 s. Surface height information is retrieved based on the ratios of red, green, and blue color information. A new vector projection technique has developed to enhance the vertical resolution of the measurement. The measurement accuracy of the prototype system has been verified via different test samples.

A smartphone-centric system for the range of motion assessment in stroke patients.

The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.

Method for real-time critical dimensions signature monitoring and control: sensor, actuator, and experimental results.

We present in this paper a system and method for real-time monitoring and control of critical dimensions (CD) signature profile in lithography. The proposed system involves the development and integration of a scatterometry system, a programmable multi-zone thermal processing system, and control system software. Based on scatterometry, the intensity and phase of the reflected light from the resist film are measured at a fixed incident angle and across multiple wavelengths. A programmable thermal processing system is then used to adjust the processing temperature during post-exposure baking in lithography to achieve the desired CD signature profile. Experimental results demonstrate the feasibility of the proposed approach. An improvement of CD signature control of 85% is achieved in terms of the mean square error with and without control.