Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions

Abstract

BackgroundAssessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.MethodsThe study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.ResultsThe reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from either accelerometer position.ConclusionsMachine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition, significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both locations.

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

“…Such movements can be measured with an inertial measurement unit (IMU), particularly if the unit is installed near the mounding blade of the excavator. These sensors have developed into low-cost devices due to their extensive use in numerous applications, including robotics (Botero Valencia et al 2017;Menna et al 2017), vibration detection (Singleton et al 2017;Sabato et al 2017), and human activity recognition (Pavey et al 2017;Zdravevski et al 2017). As the mounding blade of the excavator usually penetrates the soil surface at the same angle between the mounds, the subsequent vibrations and shocks to the excavator boom may correlate with the stone content of the soil.…”

“…Activity recognition based on inertial measurements is a well-researched topic, in particular for human activities (Pavey et al 2017;Zdravevski et al 2017;Hammerla et al 2016;Trost et al 2014). Typically, activity recognition is achieved by supervised machine learning, in which a training data set is obtained from the inertial measurement sensors, and then a model is trained based on the observations and the known labels for the activity.…”

Multi-sensor fusion based on multiple classifier systems for human activity identification

“…Furthermore, the method was deployed to correct the effect of activity drift in pre-impact fall detection, recognition of transition activities, human motion tracking, real-time contextaware navigation, and pedestrian location navigations. Zdravevski et al [42] developed enhanced and real-time multimodal sensor-based activity detection and monitoring using logistic regression with a fusion of inertial sensors and physiological signals. Feature concatenation methods that involve the fusion of vision based sensors and inertial sensors have also been proposed using machine learning for human activity detection and health monitoring.…”

“…We choose the small window sizes because most of the activities involved in the study are ambulatory activities that require small window sizes to recognize. These include activities such as walking, descending stairs, running or jogging [42,64]. Each of the sensor modality (accelerometer, gyroscope, and magnetometer) used in this study were separately processed by applying the linear interpolation function L() and data segmentation function Sg() developed and then saved for feature extraction.…”

“…, x S } , support vector machine optimally separate the classes into different activities. However, the support vector machine requires large training examples to avoid overfitting and extensive parameter tuning in order to obtain high-performance generalizations [42].…”