Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Assessment of Balance Instability by Wearable Sensor Systems During Postural Transitions

Several studies have demonstrated beneficial effects of real-time biofeedback for improving postural control. However, the application for daily activities, which also include postural transitions, is still limited. One crucial aspect is the time point of providing feedback, and thus its reliability. This might depend on the sensor system used, but also on how the threshold is defined. This study investigates which wearable sensor system and what kind of threshold is more reliable in a situation of a postural transition.To this end, we compared three sensor systems regarding their accuracy in timing in a stable and unstable postural transition in 16 healthy young adults: a multiple Inertial Measurement Unit system (IMU), a pressure Insoles System (IS), and a combination of both systems (COMB). Further, we contrasted two threshold parameters for each system: a Quiet Standing-based threshold (QSth) and a Limits of Stability-based threshold (LoSth).Two-way repeated measures ANOVAs and Wilcoxon tests (α = 0.05) indicated highest accuracy in the COMB LoSth, though with small differences to the IS LoSth. The LoSth showed more accurate timing than the QSth, especially in medio-lateral direction for IS and COMB.Consequently, for providing a reliable timing for a potential biofeedback applied by a wearable device in everyday life situations applications should focus on pressure insoles and a functional stability threshold, such as the LoS-based threshold.

Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm

Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.

Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor

OBJECTIVE

To estimate instantaneous oxygen uptake VO₂ with a small, low-cost wearable sensor during exercise and daily activities in order to enable monitoring of energy expenditure (EE) in uncontrolled settings. We aim to do so using a combination of seismocardiogram (SCG), electrocardiogram (ECG) and atmospheric pressure (AP) signals obtained from a minimally obtrusive wearable device.

METHODS

In this study, subjects performed a treadmill protocol in a controlled environment and an outside walking protocol in an uncontrolled environment. During testing, the COSMED K5 metabolic system collected gold standard breath-by-breath (BxB) data and a custom-built wearable patch placed on the mid-sternum collected SCG, ECG and AP signals. We extracted features from these signals to estimate the BxB VO₂ data obtained from the COSMED system.

RESULTS

In estimating instantaneous VO₂, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 ± 0.98 ml/kg/min and R² of 0.77). For the outside protocol, we achieved our best results using a combination of SCG (frequency), ECG and AP features (RMSE of 4.3 ± 1.47 ml/kg/min and R² of 0.64). In estimating VO₂ consumed over one minute intervals during the protocols, our median percentage error was 15.8[Formula: see text] for the treadmill protocol and 20.5[Formula: see text] for the outside protocol.

CONCLUSION

SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous VO₂ in both controlled and uncontrolled settings. SCG signals capturing variation in cardio-mechanical processes, AP signals, and state of the art machine learning models contribute significantly to the accurate estimation of instantaneous VO₂.

SIGNIFICANCE

Accurate estimation of VO₂ with a low cost, minimally obtrusive wearable patch can enable the monitoring of VO₂ and EE in everyday settings and make the many applications of these measurements more accessible to the general public.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.