SensorBased Daily Physical Activity: Towards Prediction of the Level of Concern about Falling in Peripheral Neuropathy Concern about falling is prevalent and increases the risk of falling in people with peripheral neuropathy (PN). However, the assessment of concern about falling relies on selfreport surveys, and thus continuous monitoring has not been possible. We investigated the influence of concern about falling on sensorbased daily physical activity among people with PN. Fortynine people with PN and various levels of concern about falling participated in this study. Physical activity outcomes were measured over a period of 48 hours using a validated chestworn sensor. The level of concern about falling was assessed using the falls efficacy scaleinternational (FESI). The low concern group spent approximately 80 min more in walking and approximately 100 min less in sittinglying compared to the high concern group. In addition, the low concern group had approximately 50% more walking bouts and step counts compared to the high concern group. Across all participants, the duration of walking bouts and total step counts was significantly correlated with FESI scores. The duration of walking bouts and total step counts may serve as eHealth targets and strategies for fall risk assessment among people with PN.

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.