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.

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.

Currently, oxygen uptake ("Equation missing"<!-- image only, no MathML or LaTex -->) is the most precise means of investigating aerobic fitness and level of physical activity; however, "Equation missing"<!-- image only, no MathML or LaTex --> 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 "Equation missing"<!-- image only, no MathML or LaTex --> 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 "Equation missing"<!-- image only, no MathML or LaTex --> dynamics during ADL. The variables derived from the wearable sensors were used to create a "Equation missing"<!-- image only, no MathML or LaTex --> predictor based on a random forest method. The "Equation missing"<!-- image only, no MathML or LaTex --> 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 "Equation missing"<!-- image only, no MathML or LaTex --> during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured "Equation missing"<!-- image only, no MathML or LaTex --> and the prediction bias was 0.2 ml·min⁻¹·kg⁻¹. The MNG calculated based on predicted "Equation missing"<!-- image only, no MathML or LaTex --> was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured "Equation missing"<!-- image only, no MathML or LaTex --> 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.

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- "Equation missing"<!-- image only, no MathML or LaTex --> and long-distance "Equation missing"<!-- image only, no MathML or LaTex --> 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.

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 (V̇o₂) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o₂ kinetics). This study evaluated aerobic system dynamics based on predicted V̇o₂ 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_HIP), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o₂. The aerobic system analysis was based on the frequency-domain analysis of ACC_HIP and predicted V̇o₂ data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC_HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o₂ data during μADL correlated with the temporal characteristics of measured V̇o₂ data during laboratory-controlled protocol ([ ] = 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.

The study of oxygen uptake (V̇o₂) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o₂measurements are not practical for general population under realistic settings. Devices to measure V̇o₂are 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 V̇o₂dynamics 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 V̇o₂response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o₂was used to train an artificial neural network to create an algorithm able to predict the V̇o₂based on easy-to-obtain inputs. The dynamics of the V̇o₂response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o₂was strongly correlated to the measured V̇o₂( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o₂dynamics 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.