Using Accelerometer and GPS Data for RealLife Physical Activity Type Detection This paper aims to examine the role of global positioning system (GPS) sensor data in reallife physical activity (PA) type detection. Thirtythree young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semistructured and reallife. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semistructured (Scenario 1) and combined (semistructured  reallife) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting nonlevel and level walking. Assessing the transferability of the models on reallife data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that kneemodels provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves reallife PA type classification performance if combined data are used for training the model. Moreover, the kneemodel provides the minimal device configuration with reliable accuracy for detecting reallife PA types.

Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology Activity recognition systems utilise data from sensors in mobile, environmental and wearable devices, ubiquitously available to individuals. It is a growing research area within intelligent systems that aims to model and identify human physical, cognitive and social actions, patterns and skills. They typically rely on supervised machinelearning approaches, in which the cost of gathering and labelling data is high due to the diverse, interleaved and dynamic nature of human behaviour. Transfer learning is an approach in which previously learned knowledge is utilised to model a new but related setting. For instance, it can reuse existing knowledge to recognise activities performed by different types of users, using different sensor technologies and in different environmental conditions. As the adoption of Internet of Thing devices increases, mobile and wearable sensing is becoming pervasive, and more challenging behaviour recognition activities are being tackled. Yet, the availability of more data does not necessarily translate to better recognition models, if these data are not properly labelled. Thus, the importance of taking advantage of transfer learning to advance the field of activity recognition. This literature review summarises the transfer learning techniques and explores the benefits of combining mobile and wearable devices with environmental sensors in support of transfer learning. We also discuss the maturity of transfer learning by analysing the validation method used in the papers reviewed. Overall, 170 selected articles published between 2014 and 2019 were reviewed following the Okali and Schabram methodology. Findings show an increase of 41% of publications when comparing the output of 2019 against the average number of papers published in the previous 5 years (2014xe2x80x932018). Inertial sensors such as accelerometers and gyroscopes, are the most frequently used. Feature and instance representation are mature techniques for transfer knowledge. Unsupervised learning across users is a typical application, and shallow techniques and active learning are areas of opportunity in transfer learning methodologies.