Energy Expenditure Estimation Using Accelerometry and Heart Rate for Multiple Sclerosis and Healthy Older Adults

Abstract

Abstract-Accurate estimation of Energy Expenditure (EE) in ambulatory settings provides greater insight into the underlying relation between different human physical activity and health. This paper describes the development and validation of energy expenditure estimation algorithms. A total of 4 healthy subjects and 3suffering from multiple sclerosis were monitored using a goldstandard energy expenditure measurement system, a heart rate monitor and accelerometry. We demonstrated that greater improvements can be achieved by estimating energy expenditure during normal activities of daily living by combining both whole body acceleration estimates, vertical body acceleration estimates, body posture and heart rate data as part of a flex heart rate algorithm in subject specific models when compared to using accelerometry or heart rate data alone. This will allow more accurate EE estimation during normal activities of daily living.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

“…Energy expenditure in a young healthy population has been studied extensively, however, applying such estimation models to older subject groups needs to be investigated. Examples on this area consider the adoption of a wrist-based device [ 99 ], a waist-attached sensor combined with a heart rate monitor validated on older adults with multiple sclerosis [ 100 ], or lower-limbs accelerometer measures [ 101 ], achieving accurate predictions.…”

Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

“…and a motion analysis video system to study the gait variability in MS subjects and conclude that there was more variability in kinematic measures for MS subjects who also reported more fatigue and walked slower compared to controls [51]. Bourke et al [13] show that energy expenditure estimation can be improved by combining it with accelerometry and heart-rate measurements. Motl et al validate accelerometry data, based on its correlations with 6MWT distance and oxygen cost of walking, as objective markers of walking limitations in MS [52].…”

“…Previous works have demonstrated the potential of using inertial gait features for quantitative mobility assessments in persons with MS. The features studied include, but are not limited to, energy expenditure estimator [13], causality index [14], stance to swing ratio [15], double support [15], Lyapunov exponent [16], and warp scores [17], each with a certain separability performance. These works primarily focus on finding gait variables that improve separability performance to distinguish between controls and MS subjects with or without reference to MS disability levels, assigned using an expanded disability status scale (EDSS) [7], and often, the relationships between inertial gait variables and clinical data are not explored.…”