Experimental analysis of artificial neural networks performance for accessing physical activity recognition in daily life

The importance of the Physical Activity (PA) study in the uncertain, dynamic environment of daily life has been drawn much attention. Traditional researches demonstrate that leveraging advanced on-body wearable sensors and effective machine learning algorithms enable monitoring and recognizing human daily PA in controlled environments with high accuracy. But they usually suffer from low accuracy and weak robustness of objectively qualifying PA in daily life due to restrictions like less-attachments, low sensitivity of consumable wearables and subject's variation. Thus, there is a lack of holistic investigation on how to improve accuracy of PA recognition in daily life using cost-effective wearable devices with feasible algorithms. In this paper, we use less-attached on-body consumable wearables like belt and wristband devices to collect data from two different groups of people(10 healthy people and 7 mild cognitive impairment patients). We compared the features from different types of sensors (accelerometer, gyroscope and magnetometer) and performed feature fusion. Then, by using Artificial Neural Network(ANN) to classify the features after fusion, we get a cost-effective wearable intelligence approaches for PA recognition in daily life. Parameters of achieving high recognition rate including time window sizes, features and activation functions. The experimental results indicate that PA containing 10 subjects collected by wrist in daily life environments with average accuracy up to 93%. In addition, ANN had good results in 7 mild cognitive impairment (MCI) patients, reaching 96%.