Sökning: "Mänsklig aktivitetsigenkänning"
Visar resultat 1 - 5 av 6 uppsatser innehållade orden Mänsklig aktivitetsigenkänning.
1. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition
Master-uppsats, KTH/Mekatronik och inbyggda styrsystemSammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER
2. Human Activity Recognition Models and Step Counter With Smartphone Sensor Data
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In this time of technology, with the availability of wearable sensors and copiousamounts of cheap data, new uses of machine learning emerge. Tasks that were previouslyheld-back by a lack of data and computation power are today more feasible and useful thanever. Human activity recognition (HAR) is one such task. LÄS MER
3. Human Activity Recognition and Step Counter Using Smartphone Sensor Data
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Human Activity Recognition (HAR) is a growing field of research concerned with classifying human activities from sensor data. Modern smartphones contain numerous sensors that could be used to identify the physical activities of the smartphone wearer, which could have applications in sectors such as healthcare, eldercare, and fitness. LÄS MER
4. Step Counter and Activity Recognition Using Smartphone IMUs
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Fitness tracking is a rapidly growing market as more people desire to take better control over their lives. And the growing availability of smartphones with sensitive sensors makes it possible for anyone to take part. LÄS MER
5. Recognizing Semantics in Human Actions with Object Detection
Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)Sammanfattning : Two-stream convolutional neural networks are currently one of the most successful approaches for human action recognition. The two-stream convolutional networks separates spatial and temporal information into a spatial stream and a temporal stream. LÄS MER