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Forzani, Erica

Publications and source records attributed to Forzani, Erica.

A Smart System for the Contactless Measurement of Energy Expenditure

Energy Expenditure (EE) (kcal/day), a key element to guide obesity treatment, is measured from CO 2 production, VCO 2 (mL/min), and/or O 2 consumption, VO 2 (mL/min). Current technologies are limited due to the requirement of wearable facial accessories. A novel system, the Smart Pad, which measures EE via VCO 2 from a room’s ambient CO 2 concentration transients was evaluated. Resting EE (REE) and exercise VCO 2 measurements were recorded using Smart Pad and a reference instrument to study measurement duration’s influence on accuracy. The Smart Pad displayed 90% accuracy (±1 SD) for 14–19 min of REE measurement and for 4.8–7.0 min of exercise, using known room’s air exchange rate. Additionally, the Smart Pad was validated measuring subjects with a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m2), successfully validating the system accuracy across REE’s measures of ~1200 to ~3000 kcal/day. Furthermore, high correlation between subjects’ VCO 2 and λ for CO 2 accumulation was observed (p < 0.00001, R = 0.785) in a 14.0 m3 sized room. This finding led to development of a new model for REE measurement from ambient CO 2 without λ calibration using a reference instrument. The model correlated in nearly 100% agreement with reference instrument measures (y = 1.06x, R = 0.937) using an independent dataset (N = 56).

47 OTHER INSTRUMENTATION↗

An Unobstructive Sensing Method for Indoor Air Quality Optimization and Metabolic Assessment within Vehicles

This work investigates the use of an intelligent and unobstructive sensing technique for maintaining vehicle cabin’s indoor air quality while simultaneously assessing the driver metabolic rate. CO 2 accumulation patterns are of great interest because CO 2 can have negative cognitive effects at higher concentrations and also since CO 2 accumulation rate can potentially be used to determine a person’s metabolic rate. The management of the vehicle’s ventilation system was controlled by periodically alternating the air recirculation mode within the cabin, which was actuated based on the CO 2 levels inside the vehicle’s cabin. The CO 2 accumulation periods were used to assess the driver’s metabolic rate, using a model that considered the vehicle’s air exchange rate. In the process of the method optimization, it was found that the vehicle’s air exchange rate (λ [h –1 ]) depends on the vehicle speeds, following the relationship: λ = 0.060 × (speed) – 0.88 when driving faster than 17 MPH. An accuracy level of 95% was found between the new method to assess the driver’s metabolic rate (1620 ± 140 kcal/day) and the reference method of indirect calorimetry (1550 ± 150 kcal/day) for a total of N = 16 metabolic assessments at various vehicle speeds. The new sensing method represents a novel approach for unobstructive assessment of driver metabolic rate while maintaining indoor air quality within the vehicle cabin.

passive sensing↗