Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts

Using Thermal and Contact Sensors for Mood Detection in Smart Living Environments

Mental health in everyday life can be supported using ambient and wearable sensors, which could detect when a person is feeling well (e.g. happy or calm) or not feeling well (e.g. stressed or sad). Identifying positive or negative moods can be useful for interventions to reinforce or improve them respectively. Previous work that used data from wearable sensors (accelerometers) in a personalized way successfully identified mood while users performed Activities of Daily Living (ADLs). This paper presents a general approach to using data from ambient (contact and thermal) sensors to identify the mood of users while performing ADLs. The rationale for using ambient sensors is their non-intrusiveness. Users do not have to be as concerned about their use or maintenance as with wearable sensors. Data was collected from 15 participants performing ADLs in 7 sessions. Accuracy classification results obtained with 7 algorithms underperformed, with the highest value being 45.74% with Random Forest in 10-Fold Cross Validation. For future work, data from other sensors (in addition to the accelerometer) will be collected to support the improvement of the personalized approach presented, and other evaluation metrics (e.g. F1-score and AUC) will be used