Comprehensive assessment of gesture production: a new test of upper limb apraxia (TULIA).

BACKGROUND: Only few standardized apraxia scales are available and they do not cover all domains and semantic features of gesture production. Therefore, the objective of the present study was to evaluate the reliability and validity of a newly developed test of upper limb apraxia (TULIA), which is comprehensive and still short to administer. METHODS: The TULIA consists of 48 items including imitation and pantomime domain of non-symbolic (meaningless), intransitive (communicative) and transitive (tool related) gestures corresponding to 6 subtests. A 6-point scoring method (0-5) was used (score range 0-240). Performance was assessed by blinded raters based on videos in 133 stroke patients, 84 with left hemisphere damage (LHD) and 49 with right hemisphere damage (RHD), as well as 50 healthy subjects (HS). RESULTS: The clinimetric findings demonstrated mostly good to excellent internal consistency, inter- and intra-rater (test-retest) reliability, both at the level of the six subtests and at individual item level. Criterion validity was evaluated by confirming hypotheses based on the literature. Construct validity was demonstrated by a high correlation (r = 0.82) with the De Renzi-test. CONCLUSION: These results show that the TULIA is both a reliable and valid test to systematically assess gesture production. The test can be easily applied and is therefore useful for both research purposes and clinical practice.

Are cuffless devices challenged enough? Design of a validation protocol for ambulatory blood pressure monitors at the wrist: the case of the Aktiia Bracelet.

The US and European guidelines for the diagnosis and management of hypertension recommend the introduction of systematic home and night Blood Pressure (BP) monitoring. Fully-automated wearable devices can address the needs of patients and clinicians by improving comfort while achieving measurement accuracy. Often located at the wrist and based on indirect BP measurements, these devices must address the challenges of ambulatory scenarios. New validation strategies are needed, but little guidance has been published so far.In this work, we propose an experimental protocol for the validation of cuffless wrist BP monitors that addresses ambulatory environment challenges in a controlled experimental setting. The protocol assesses the robustness of the measurement for different body postures, the ability of the device to track BP changes, and its ability to deal with hydrostatic pressure changes induced by different arm heights.Performance testing using Aktiia Bracelet is provided as an illustration. The results of this pilot study indicate that the Aktiia Bracelet can generate accurate BP estimates for sitting and lying positions and is not affected by hydrostatic pressure perturbations.Clinical Relevance- Automated cuffless BP monitoring is opening a new chapter in the way patients are being diagnosed and managed. This paper provides a guidance on how to assess the clinical utility of such devices when used in different body positions.

Electrical stimulation induces propagated colonic contractions in an experimental model.

BACKGROUND: Direct colonic electrical stimulation may prove to be a treatment option for specific motility disorders such as chronic constipation. The aim of this study was to provoke colonic contractions using electrical stimulation delivered from a battery-operated device. METHODS: Electrodes were inserted into the caecal seromuscular layer of eight anaesthetized pigs. Contractions were induced by a neurostimulator (Medtronic 3625). Caecal motility was measured simultaneously by video image analysis, manometry and a technique assessing colonic transit. RESULTS: Caecal contractions were generated using 8-10 V amplitude, 1000 micros pulse width, 120 Hz frequency for 10-30 s, with an intensity of 7-15 mA. The maximal contraction strength was observed after 20-25 s. Electrical stimulation was followed by a relaxation phase of 1.5-2 min during which contractions propagated orally and aborally over at least 10 cm. Spontaneous and stimulated caecal motility values were significantly different for both intraluminal pressure (mean(s.d.) 332(124) and 463(187) mmHg respectively; P < 0.001, 42 experiments) and movement of contents (1.6(0.9) and 3.9(2.8) mm; P < 0.001, 40 experiments). CONCLUSION: Electrical stimulation modulated caecal motility, and provoked localized and propagated colonic contractions.

Real-time gait analysis with accelerometer-based smart shoes.

In this paper, we present the evaluation of a new smart shoe capable of performing gait analysis in real time. The system is exclusively based on accelerometers which minimizes the power consumption. The estimated parameters are activity class (rest/walk/run), step cadence, ground contact time, foot impact (zone, strength, and balance), forward distance, and speed. The different parameters have been validated with a customized database of 26 subjects on a treadmill and video data labeled manually. Key measures for running analysis such as the cadence is retrieved with a maximum error of 2%, and the ground contact time with an average error of 3.25%. The classification of the foot impact zone achieves a precision between 72% and 91% depending of the running style. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.

Towards VO2 monitoring: Validation of a heart rate based algorithm.

This article presents and validates a novel algorithm for the continuous monitoring of the VO2 during exercise. The algorithm relies on instantaneous HR measurements to provide a continuous estimation, and can be integrated in a wearable device (e.g., smartwatch, sensor patch). It can be customized by user's main anthropomorphic parameters and automatically learns from newly incoming data recalibrating itself if needed. The system is evaluated against a database of 14 healthy subjects performing various maximal endurance tests. The proposed method provides a VO2 estimation with average RMSE of 4.63 ml/kg/min.

An autonomous medical monitoring system: Validation on arrhythmia detection.

In this paper, we present a generic platform for autonomous medical monitoring and diagnostics. We validated the platform in the context of arrhythmia detection with publicly available databases. The big advantage of this platform is its capacity to deal with various types of physiological signals. Many pre-processing steps are performed to bring the input information into a uniform state that will be explored by a machine learning algorithm. Since this block plays a crucial role in the entire processing pipeline, three different methods were evaluated for detection and classification of anomalies. The results presented in this work are validated on cardiac beats, where the highest accuracy was obtained on the classification of normal beats (94%). On the other hand, atrial fibrillation and premature ventricular contraction beats were classified with an accuracy of 78%.

Viral Escape in the Central Nervous System with Multidrug-Resistant Human Immunodeficiency Virus-1.

In this study, we report the case of a patient infected with human immunodeficiency virus (HIV)-1 who developed ataxia and neurocognitive impairment due to viral escape within the central nervous system (CNS) with a multidrug-resistant HIV-1 despite long-term viral suppression in plasma. Antiretroviral therapy optimization with drugs with high CNS penetration led to viral suppression in the CSF, regression of ataxia, and improvement of neurocognitive symptoms.

Towards an unsupervised device for the diagnosis of childhood pneumonia in low resource settings: automatic segmentation of respiratory sounds.

Pneumonia remains the worldwide leading cause of children mortality under the age of five, with every year 1.4 million deaths. Unfortunately, in low resource settings, very limited diagnostic support aids are provided to point-of-care practitioners. Current UNICEF/WHO case management algorithm relies on the use of a chronometer to manually count breath rates on pediatric patients: there is thus a major need for more sophisticated tools to diagnose pneumonia that increase sensitivity and specificity of breath-rate-based algorithms. These tools should be low cost, and adapted to practitioners with limited training. In this work, a novel concept of unsupervised tool for the diagnosis of childhood pneumonia is presented. The concept relies on the automated analysis of respiratory sounds as recorded by a point-of-care electronic stethoscope. By identifying the presence of auscultation sounds at different chest locations, this diagnostic tool is intended to estimate a pneumonia likelihood score. After presenting the overall architecture of an algorithm to estimate pneumonia scores, the importance of a robust unsupervised method to identify inspiratory and expiratory phases of a respiratory cycle is highlighted. Based on data from an on-going study involving pediatric pneumonia patients, a first algorithm to segment respiratory sounds is suggested. The unsupervised algorithm relies on a Mel-frequency filter bank, a two-step Gaussian Mixture Model (GMM) description of data, and a final Hidden Markov Model (HMM) interpretation of inspiratory-expiratory sequences. Finally, illustrative results on first recruited patients are provided. The presented algorithm opens the doors to a new family of unsupervised respiratory sound analyzers that could improve future versions of case management algorithms for the diagnosis of pneumonia in low-resources settings.

Real-time monitoring of swimming performance.

This article presents the performance results of a novel algorithm for swimming analysis in real-time within a low-power wrist-worn device. The estimated parameters are: lap count, stroke count, time in lap, total swimming time, pace/speed per lap, total swam distance, and swimming efficiency (SWOLF). In addition, several swimming styles are automatically detected. Results were obtained using a database composed of 13 different swimmers spanning 646 laps and 858.78 min of total swam time. The final precision achieved in lap detection ranges between 99.7% and 100%, and the classification of the different swimming styles reached a sensitivity and specificity above 98%. We demonstrate that a swimmers performance can be fully analyzed with the smart bracelet containing the novel algorithm. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.

Accurate walking and running speed estimation using wrist inertial data.

In this work, we present an accelerometry-based device for robust running speed estimation integrated into a watch-like device. The estimation is based on inertial data processing, which consists in applying a leg-and-arm dynamic motion model to 3D accelerometer signals. This motion model requires a calibration procedure that can be done either on a known distance or on a constant speed period. The protocol includes walking and running speeds between 1.8km/h and 19.8km/h. Preliminary results based on eleven subjects are characterized by unbiased estimations with 2(nd) and 3(rd) quartiles of the relative error dispersion in the interval ±5%. These results are comparable to accuracies obtained with classical foot pod devices.

Physical activity profiling: Activity-specific step counting and energy expenditure models using 3D wrist acceleration.

In this paper, we present the evaluation of a new physical activity profiling system embedded in a wrist-located device. We propose a step counting and an energy expenditure (EE) method, and evaluate their accuracy against gold standard references. To this end, we used an actimetry sensor on the waist and an indirect calorimetry monitoring device on a population of 13 subjects to obtain step count and metabolic equivalent task (kcal/kg/h) referenced values. The subjects followed a protocol that spanned a given set of activities (lying, standing, walking, running) at a wide range of intensities. The performance of the EE model was characterized by a root-mean-square error (RMSE) of 1.22±0.34kcal/min, and step-count model at regular walking/running speeds by 0.71±0.06step/10sec.

Validation of a wrist monitor for accurate estimation of RR intervals during sleep.

While the incidence of sleep disorders is continuously increasing in western societies, there is a clear demand for technologies to asses sleep-related parameters in ambulatory scenarios. The present study introduces a novel concept of accurate sensor to measure RR intervals via the analysis of photo-plethysmographic signals recorded at the wrist. In a cohort of 26 subjects undergoing full night polysomnography, the wrist device provided RR interval estimates in agreement with RR intervals as measured from standard electrocardiographic time series. The study showed an overall agreement between both approaches of 0.05 ± 18 ms. The novel wrist sensor opens the door towards a new generation of comfortable and easy-to-use sleep monitors.