Sensing leg movement enhances wearable monitoring of energy expenditure

Abstract

Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.

Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost

“…2 bottom row). These results align with the previous data-driven approach showing improved accuracy [52] and estimation time [53].…”

The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors

“…However, Slade et al have used systems based on two inertial sensors (shank and thigh) for the estimation of energy expenditure, which produces a big improvement. Even using a single sensor on the thigh can significantly reduce error in the estimation of energy expenditure obtained by the smartwatch [ 19 ].…”

“…Devices, such as the Jawbone Up3 (Jowbone, San Francisco, CA, USA) and Fitbit Surge (Fitbit, San Francisco, CA, USA), showed positive correlations greater than 0.8 when using step counting or accelerometer steps; however, energy expenditure was underestimated compared to indirect and direct calorimetry [ 16 ]. Furthermore, there is no doubt that a system based on biomechanical analysis is more reliable for the estimation of energy expenditure, with an error under 13%, which is very far from the 44% obtained by the smartwatch [ 19 ]. However, the use of such a system on a daily basis could be a limitation for the end-user to carry multiple recording devices instead of just one.…”

“…This would produce a more precise system of energy expenditure calculation for a greater number of activities. This would be essential to compare the energy expenditure obtained by equations with the VO2 via indirect calorimetry [ 19 , 47 ]. The applications of the proposed method can be related to any EE estimation for physical activities, such as physical activity promotion for walking/running, clinical applications, like sedentary behaviour change for health care, and technical use for losing weight.…”

“…These differences are smaller when the outcome variable heart rate is taken as a reference [ 18 ]. Likewise, specific device designs based on inertial sensors for energy expenditure estimation compared to gas analysis can obtain better results than those obtained by a conventional smartwatch or an activity-specific smartwatch [ 19 ]. Despite the improvement in the results based on specific systems, there are still differences in the estimation of energy expenditure that could be reduced.…”