A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min−1·kg−1. The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

Cardiovascular monitoring is important to prevent diseases from progressing. The jugular venous pulse (JVP) waveform offers important clinical information about cardiac health, but is not routinely examined due to its invasive catheterisation procedure. Here, we demonstrate for the first time that the JVP can be consistently observed in a non-contact manner using a photoplethysmographic imaging system. The observed jugular waveform was strongly negatively correlated to the arterial waveform (r = −0.73 ± 0.17), consistent with ultrasound findings. Pulsatile venous flow was observed over a spatially cohesive region of the neck. Critical inflection points (c, x, v, y waves) of the JVP were observed across all participants. The anatomical locations of the strongest pulsatile venous flow were consistent with major venous pathways identified through ultrasound.

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Melanoma is the most deadly form of skin cancer and it is costly for dermatologists to screen every patient for melanoma. There is a need for a system to assess the risk of melanoma based on dermatological photographs of a skin lesion. However, the presence of illumination variation in the photographs can have a negative impact on lesion segmentation and classification performance. A novel multistage illumination modeling algorithm is proposed to correct the underlying illumination variation in skin lesion photographs. The first stage is to compute an initial estimate of the illumination map of the photograph using a Monte Carlo nonparametric modeling strategy. The second stage is to obtain a final estimate of the illumination map via a parametric modeling strategy, where the initial nonparametric estimate is used as a prior. Finally, the corrected photograph is obtained using the final illumination map estimate. The proposed algorithm shows better visual, segmentation, and classification results when compared to three other illumination correction algorithms, one of which is designed specifically for lesion analysis.

The study of oxygen uptake (V̇o) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o measurements are not practical for general population under realistic settings. Devices to measure V̇o are associated with elevated cost, uncomfortable use of a mask, need of trained technicians, and impossibility of long-term data collection. The objective of this study was to predict the V̇o dynamics from heart rate and inputs from the treadmill ergometer by a novel artificial neural network approach. To accomplish this, 10 healthy young participants performed one incremental and three moderate constant work rate treadmill walking exercises. The speed and grade used for the moderate-intensity protocol was related to 80% of the V̇o response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o was used to train an artificial neural network to create an algorithm able to predict the V̇o based on easy-to-obtain inputs. The dynamics of the V̇o response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o was strongly correlated to the measured V̇o ( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o dynamics during treadmill walking exercise. The proposed algorithm could be incorporated into smart devices and fitness equipment, making them suitable for tracking changes in aerobic fitness and physical health beyond the infrequent monitoring of patients during clinical interventions and rehabilitation programs.

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o kinetics). This study evaluated aerobic system dynamics based on predicted V̇o data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o. The aerobic system analysis was based on the frequency-domain analysis of ACC and predicted V̇o data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o data during μADL correlated with the temporal characteristics of measured V̇o data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Photoplethysmography (PPG) devices are widely used for monitoring cardiovascular function. However, these devices require skin contact, which restricts their use to at-rest short-term monitoring. Photoplethysmographic imaging (PPGI) has been recently proposed as a non-contact monitoring alternative by measuring blood pulse signals across a spatial region of interest. Existing systems operate in reflectance mode, many of which are limited to short-distance monitoring and are prone to temporal changes in ambient illumination. This paper is the first study to investigate the feasibility of long-distance non-contact cardiovascular monitoring at the supermeter level using transmittance PPGI. For this purpose, a novel PPGI system was designed at the hardware and software level. Temporally coded illumination (TCI) is proposed for ambient correction, and a signal processing pipeline is proposed for PPGI signal extraction. Experimental results show that the processing steps yielded a substantially more pulsatile PPGI signal than the raw acquired signal, resulting in statistically significant increases in correlation to ground-truth PPG in both short- and long-distance monitoring. The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.