Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Vyas, Nisarg; Farringdon, Jonathan; Andre, David; Stivoric, John

doi:10.1609/aimag.v33i2.2408

Cited by 31 publications

(34 citation statements)

References 21 publications

(29 reference statements)

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“…This paper presents a system that recognizes the user's activity and detects falls using wearable sensors. To provide timely and appropriate assistance, AAL systems must understand the user's situation and context, making activity recognition (AR) an essential component [4,5]. Fall detection (FD) is an important component of many AAL systems because approximately half of the hospitalizations of the elderly are caused by falls [6], fear of falling is an important cause for nursing home admission [7], and "the long lie" (not being able to get up and call for help) is a good predictor of death within six months [8].…”

Section: Introductionmentioning

confidence: 99%

Efficient Activity Recognition and Fall Detection Using Accelerometers

Kozina

Gjoreski

Gams

et al. 2013

Communications in Computer and Information Science

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Abstract. Ambient assisted living (AAL) systems need to understand the user's situation, which makes activity recognition an important component. Falls are one of the most critical problems of the elderly, so AAL systems often incorporate fall detection. We present an activity recognition (AR) and fall detection (FD) system aiming to provide robust real-time performance. It uses two wearable accelerometers, since this is probably the most mature technology for such purpose. For the AR, we developed an architecture that combines rules to recognize postures, which ensures that the behavior of the system is predictable and robust, and classifiers trained with machine learning algorithms, which provide maximum accuracy in the cases that cannot be handled by the rules. For the FD, rules are used that take into account high accelerations associated with falls and the recognized horizontal orientation (e.g., falling is often followed by lying). The system was tested on a dataset containing a wide range of activities, two different types of falls and two events easily mistaken for falls. The Fmeasure of the AR was 99 %, even though it was never tested on the same persons it was trained on. The F-measure of the FD was 78 % due to the difficulty of the events to be recognized and the need for real-time performance, which made it impossible to rely on the recognition of long lying after a fall.

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Section: Introductionmentioning

confidence: 99%

Efficient Activity Recognition and Fall Detection Using Accelerometers

Kozina

Gjoreski

Gams

et al. 2013

Communications in Computer and Information Science

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show abstract

“…[1][2][3][4][5] The benefits of detecting physical activities (PAs) using wearable devices include the ability to track regular PA, provide accurate energy expenditure (EE) estimation and assist in behavioral modifications that may lead to a healthier active lifestyle in community settings. [6][7][8][9][10] However, there are only a limited number of studies that have detected and classified PAs performed by individuals who rely on wheelchairs for mobility using wearable devices.…”

Section: Introductionmentioning

confidence: 99%

Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury

et al. 2013

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Study design: Validation. Objectives: The primary aim of this study was to develop and evaluate activity classification algorithms for a multisensor-based SenseWear (SW) activity monitor that can recognize wheelchair-related activities performed by manual wheelchair users (MWUs) with spinal cord injury (SCI). The secondary aim was to evaluate how the accuracy in activity classification affects the estimation of energy expenditure (EE) in MWUs with SCI. Setting: University-based laboratory. Methods: Forty-five MWUs with SCI wore a SW on their upper arm and participated in resting, wheelchair propulsion, arm-ergometery and deskwork activities. The investigators annotated the start and end of each activity trial while the SW collected multisensor data and a portable metabolic cart collected criterion EE. Three methods including linear discriminant analysis, quadratic discriminant analysis (QDA), and Naïve Bayes (NB) were used to develop classification algorithms for four activities based on the training data set from 36 subjects. Results: The classification accuracy was 96.3% for QDA and 94.8% for NB when the classification algorithms were tested on the validation data set from nine subjects. The average EE estimation errors using the activity-specific EE prediction model were 5.3±21.5% and 4.6±22.8% when the QDA and NB classification algorithms were applied, respectively, as opposed to 4.9±20.7% when 100% classification accuracy was assumed. Conclusion: The high classification accuracy and low EE estimation errors suggest that the SW can be used by researchers and clinicians to classify and estimate the EE for the four activities tested in this study among MWUs with SCI.

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“…Staudenmayer et al [17] used a single axis accelerometer and datadriven models based on artificial neural networks to estimate physical activity metabolic equivalents (MET) and to classify activity into low-level activities, locomotion, vigorous sports, and household activities. Vyas et al [19] show how the BodyMedia FIT (now Jawbone) armband system was able to track EE by using machine learning methods that fused the information of several sensors (e.g., skin temperature, galvanic skin response, heat flux, acceleration). Ruch et al [15] compared two machine learning approaches for estimating EE in children.…”

Section: Related Workmentioning

confidence: 99%

ActiDote – A wireless sensor-based system for self-tracking activity levels among manual wheelchair users

Satizábal¹,

Grillon²,

Picasso³

et al. 2017

EAI Endorsed Transactions on Pervasive Health and Technology

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ActiDote -activity as an antidote-is a system for manual wheelchair users that takes advantage of wireless sensors to recognize activities of various intensity levels in order to allow self-tracking of the physical activity. In this paper, we describe both the hardware setup and the software pipeline that enable our system to operate. Laboratory tests using multi-modal fusion and machine learning reveal promising results on classifying activity levels and assessing energy expenditure during wheelchair propulsion on ramps of different slopes and speeds. Our results indicate that it is possible to implement a system that uses the accelerometer of a smartphone as the only sensor in the wheelchair, i.e., by attaching it to the wheelchair frame. Additionally, the user might wear a smartwatch equipped with an accelerometer to enrich the system and enhance its performance.

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Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Cited by 31 publications

References 21 publications

Efficient Activity Recognition and Fall Detection Using Accelerometers

Efficient Activity Recognition and Fall Detection Using Accelerometers

Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury

ActiDote – A wireless sensor-based system for self-tracking activity levels among manual wheelchair users

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