Estimation of energy expenditure using accelerometers and activity-based energy models—validation of a new device

Abstract

Over the last few years, the estimation of energy expenditure with accelerometers has become more and more accurate due to improvements in sensor technology. Significant enhancement could be reached by model-based estimation regarding different activity types. The kmsMove-sensor (movisens GmbH, Karlsruhe, Germany) is a device that is used to compute human energy expenditure using motiondependent calculation models. It is outfitted with an accelerometer to measure body acceleration during certain movements and activities. To validate its accuracy, the sensor was compared to indirect calorimetry as criterion measure. For this experiment, nine subjects (all males, age 46.4±10.9 years, 28-64 years) were equipped with the kmsMove-sensor as well as a portable indirect calorimeter and their energy consumption was measured over a time period of 100 min. Additionally, the energy consumption of seven out of the above-mentioned nine subjects was measured over an average of 7 h. The measurements took place in a rehabilitation clinic, where the subjects completed their regular daily rehabilitation activities. An analysis of the data revealed ICCs between the kmsMovesensor and indirect calorimetry for the time period of 100 min of 0.82 (0.38-0.96; p=0.003) and for an averaged measuring time of 7 h of 0.81 (0.22-0.97; p=0.01). Furthermore, a Bland-Altman analysis for the time period of 100 min led to a difference of the means of 4.3 kcal (limits of agreement: −94.3 and 102.9 kcal) and for the time period of an average of 7 h to −14.0 kcal (limits of agreement: −320.0 and 292.0 kcal). These findings indicate that the kmsMovesensor is an appropriate measuring device with relatively good accuracy to assess human energy expenditure in rehabilitation patients. However, this study has some limiting aspects (small sample size, artificial setting) which could influence validity.

Acute and medium term effects of a 10-week running intervention on mood state in apprentices

“…The raw data after a complete measurement was transmitted to a computer via an USB 2.0 interface. Mathematical and statistical procedures applied to the raw data can be used to differentiate between several activities: rest (standing, lying, or sitting), cycling/ergometer, climbing stairs, walking (jogging, slow, or normal walking), and “unknown activities” (for details see Härtel et al, 2011). For this study the raw data captured by the accelerometer was used to calculate the activity intensity in mg.…”

Quantification of activity in domestic cats (Felis catus) by accelerometry

“…Changes in overall activity have been linked to illness Brown et al, 2010), injury, hunting, stress/stereotypical behaviours (Papailiou et al, 2008), and energy expenditure (Hartel et al, 2011) and reproductive events such as oestrus (Gerall et al, 1973;Wielebnowski and Brown, 1998;At-Taras and Spahr, 2001;Moreira et al, 2001;Brown et al, 2002;Cornou, 2006).…”

“…Over the past decade accelerometer technology has advanced considerably, with the devices becoming smaller, cheaper and easier to use. Consequently, accelerometers are increasingly used as a tool to quantify activity in a variety of species including humans (Hendelman et al, 2000;Trost et al, 2000;Kumahara et al, 2004;Penpraze et al, 2006 Hartel et al, 2011), rhesus monkeys (Macaca mulatta) (Papailiou et al, 2008), dairy cows (At-Taras and Spahr, 2001;McGowan et al, 2007), dogs (Canis familiaris) Brown et al, 2010;Yam et al, 2011;Singh, 2013), and cats (Watanabe et al, 2005;Lascelles et al, 2007;Lascelles et al, 2008).…”

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

“…Consequently, the last decade has witnessed tremendous efforts in utilizing smart technologies such as BSNs for health monitoring and diagnosis through physical activity monitoring/assessment. Recent years have seen considerable research demonstrating the potential of BSNs in a variety of physical activity monitoring applications such as activity recognition [9,10,11,15,16,17], activity level estimation [18], caloric expenditure calculation [19,20], joint angle estimation [21], activity-based prompting [53,54,55,56,57,58], medication adherence assessment [59,60], crowd sensing [61,62,63,64,65,66], social networking [67,68,69,70], and sports training [22,23,24,25,26].…”