The feasibility of a vision-based sensor for longitudinal monitoring of mobility in older adults with dementia.

BACKGROUND: Gait and balance functions decline through the course of dementia, and can serve as a marker of changes in physical status and falls risk. We have developed a technology (AMBIENT), based on a vision-based sensor, which enables the frequent, accurate, and unobtrusive measurement of gait and balance. OBJECTIVE: The objective of this study was to examine the feasibility of using AMBIENT technology for frequent assessment of mobility in people with dementia within an inpatient setting. In particular, we examined technical feasibility, and the feasibility of participant recruitment, data collection and analysis. METHODS: AMBIENT was installed in a specialized dementia inpatient unit. AMBIENT captured gait bouts as the participants walked within the view of the sensor during their daily routine and computed the spatiotemporal parameters of gait. RESULTS: Twenty participants (age: 76.9 ± 6.7 years, female: 50%) were recruited over a period of 6 months. We recorded a total of 3843 gait bouts, of which 1171 could be used to extract gait data. On average, 58 ± 47 walking sequences per person were collected over a recording period of 28 ± 20 days. We were able to consistently extract six quantitative parameters of gait, consisting of stride length, stride time, cadence, velocity, step length asymmetry, and step time asymmetry. SIGNIFICANCE: This study demonstrates the feasibility of longitudinal tracking of gait in a dementia inpatient setting. This technology has important potential applications in monitoring functional status over time, and the development of dynamic falls risk assessments.

Heart rate variability in pulmonary hypertension with and without sleep apnea.

OBJECTIVES: Our aims were to evaluate HRV in pulmonary hypertension (WHO Group 1 and 4) compared to control subjects, and to assess whether the presence of sleep apnea in those with pulmonary hypertension would be deleterious and cause greater impairment in HRV. METHODS: This retrospective case-control study analyzed electrocardiogram segments obtained from diagnostic polysomnography. RESULTS: Forty-one pulmonary hypertension patients were compared to 41 age, sex and apnea-hypopnea index matched healthy controls. The pulmonary hypertension group had decreased high frequency, very low frequency, low frequency, and percentage of normal R-R intervals that differ by > 50 ms compared to control subjects. Moderate to severe right ventricle dysfunction on echocardiography was a predictor of lower high frequency in pulmonary hypertension patients. CONCLUSIONS: There were no differences in any HRV measures in pulmonary hypertension patients with or without sleep apnea. Impaired HRV was demonstrated in pulmonary hypertension patients however, the presence of sleep apnea did not appear to further reduce vagal modulation.

Detecting inspiratory flow limitation with temporal features of nasal airflow.

BACKGROUND: Inspiratory flow limitation is a breathing pattern during sleep caused by upper airway (UA) narrowing that occurs during snoring and various degrees of obstructive sleep apnea (OSA). Clinical examination of flow limitation relies on identifying patterns of airflow contour, however this process is subjective and lacks physiological evidence of UA narrowing. Our objective is to derive the temporal features of nasal airflow contour that characterize flow limitation. The features that correlate with UA narrowing can be used to develop machine learning classifiers to detect flow limitation with physiological support. METHODS: Sixteen healthy adult men underwent full daytime polysomnography where the nasal airflow was recorded. Before and after sleep, we measured UA anatomical parameters including neck circumference (NC) and upper-airway cross-sectional area (UA-XSA). We extracted various temporal features of airflow and investigated their relationships with the UA anatomical parameters. RESULTS: We found that three features were correlated with the anatomical parameters associated with UA narrowing: deviation index vs. baseline UA-XSA (r = -0.67, p = 0.01), peak amplitude variability vs. baseline UA-XSA (r = -0.69, p < 0.01), peak amplitude variability vs. ΔNC (r = 0.74, p < 0.01) and peak number vs. baseline UA-XSA (r = -0.54, p = 0.04). CONCLUSIONS: Temporal features of airflow were associated with UA narrowing. Future studies could utilize the features to develop classifiers to detect flow limitation and assess the severity of breathing disorders during sleep in high-risk populations such as pregnant women and children.

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy.

Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

Validating an Algorithm for Automatic Scoring of Inspiratory Flow Limitation Within a Range of Recording Settings.

Inspiratory Flow Limitation (IFL) is a phenomenon associated with narrowing of the upper airway, preventing an increase in inspiratory airflow despite an elevation in intrathoracic pressure. It has been shown that quantification of IFL might complement information provided by standard indices such as the apnea-hypopnea index (AHI) in characterizing sleep disordered breathing and identifying subclinical disease. Defining guidelines for visual scoring of IFL has been of increasing interest, and automated methods are desirable to avoid inter-scorer variability and allow analysis of large datasets. In addition, as recording instrumentation and practices may vary across hospitals and laboratories, it is useful to assess the influence of the recording parameters on the accuracy of the automated classification. We employed nasal pressure signals recorded as part of polysomnography (PSG) studies in 7 patients. Two experts independently classified approximately 2000 breaths per subject as IFL or non-IFL, and we used the consensus scoring as the gold standard. For each breath, we derived features indicative of the shape and frequency content of the signals and used them to train and validate a Support Vector Machine (SVM) to distinguish IFL from non-IFL breaths. We also assessed the effect of signal filtering (down-sampling and baseline-removal) on classification performance. The performance of the classifier was excellent (accuracy ~93%) for the raw signals (collected at 125 Hz with no filtering), and decreased for increasing high-pass cut-off frequencies (fc = [0.05, 0.1, 0.15, 0.2] Hz) down to 84% for fc= 0.2 Hz and for decreasing sampling rate (fs = [20, 50, 75, 100] Hz) down to ~85% for fs=20 Hz. Loss of performance was minimized when the classifier was re-trained using data with matched filtering characteristics (accuracy > 89%). We can conclude that the SVM feature-based algorithm provides a reliable and efficient tool for breath-by-breath classification.