Predicting epileptic seizures with a stacked long short-term memory network

Despite advancements, seizure detection algorithms are trained using only the data recorded frompast epileptic seizures. This one-dimensional approach has led to an excessive false detection rate,where common movements are incorrectly classified. Therefore, a new method of detection isrequired that can distinguish between the movements observed during a generalized tonic-clonic(GTC) seizure and common everyday activities. For this study, eight healthy participants and twodiagnosed with epilepsy simulated a series of activities that share a similar set of spatialcoordinates with an epileptic seizure. We then trained a stacked, long short-term memory (LSTM)network to classify the different activities. Results show that our network successfullydifferentiated the types of movement, with an accuracy score of 94.45%. These findings present amore sophisticated method of detection that correlates a wearers movement against 12 seizurerelated activities prior to formulating a prediction

PARCIV: Recognizing physical activities having complex interclass variations using semantic data of smartphone

Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real‐time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two‐layered approach called PARCIV that first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two‐layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine‐grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities. PARCIV recognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self‐collected dataset. Furthermore, PARCIV achieve 95% accuracy on the publicly available dataset used by state‐of‐the‐art studies. PARCIV outperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities

Multi-Dimensional Task Recognition for Human-Robot Teaming

Human-robot teams involve humans and robots collaborating to achieve tasks under various environmental conditions. Successful teaming requires robots to adapt autonomously in real-time to a human teammate's state. An important element of such adaptation is the ability for the robot to infer the tasks performed by their human teammates. Human-robot teams often perform a wide variety of tasks, involving multiple activity components, and may even perform two or more tasks concurrently. A robot’s ability to recognize the human’s composite tasks that occur concurrently is a key requirement for realizing successful collaboration. Existing task recognition algorithms are not viable for human-robot teams, as they only detect tasks from a subset of activity components and rarely detect concurrent, composite tasks. This dissertation developed a multi-dimensional task recognition algorithm capable of detecting concurrent, composite tasks across the cognitive, speech, auditory, visual, gross motor, fine-grained motor, and tactile components by incorporating metrics that are sensitive, versatile, and suitable across human-robot teaming paradigms. The developed algorithm addresses a foundational problem of understanding an individual's task engagement state in human-robot teams operating in dynamic, unstructured environments