Human activity recognition: Review, taxonomy and open challenges
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains,
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …
Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them
T PlÖtz - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
With the widespread proliferation of (miniaturized) sensing facilities and the massive growth
and popularity of the field of machine learning (ML) research, new frontiers in automated …
and popularity of the field of machine learning (ML) research, new frontiers in automated …
Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos
Parkinson's disease (PD) is a brain disorder that primarily affects motor function, leading to
slow movement, tremor, and stiffness, as well as postural instability and difficulty with …
slow movement, tremor, and stiffness, as well as postural instability and difficulty with …
Imugpt 2.0: Language-based cross modality transfer for sensor-based human activity recognition
One of the primary challenges in the field of human activity recognition (HAR) is the lack of
large labeled datasets. This hinders the development of robust and generalizable models …
large labeled datasets. This hinders the development of robust and generalizable models …
X-char: A concept-based explainable complex human activity recognition model
End-to-end deep learning models are increasingly applied to safety-critical human activity
recognition (HAR) applications, eg, healthcare monitoring and smart home control, to reduce …
recognition (HAR) applications, eg, healthcare monitoring and smart home control, to reduce …
Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals
It is only a matter of time until autonomous vehicles become ubiquitous; however, human
driving supervision will remain a necessity for decades. To assess the driver's ability to take …
driving supervision will remain a necessity for decades. To assess the driver's ability to take …
Approaching the real-world: Supporting activity recognition training with virtual imu data
Recently, IMUTube introduced a paradigm change for bootstrapping human activity
recognition (HAR) systems for wearables. The key idea is to utilize videos of activities to …
recognition (HAR) systems for wearables. The key idea is to utilize videos of activities to …
Complex deep neural networks from large scale virtual imu data for effective human activity recognition using wearables
Supervised training of human activity recognition (HAR) systems based on body-worn
inertial measurement units (IMUs) is often constrained by the typically rather small amounts …
inertial measurement units (IMUs) is often constrained by the typically rather small amounts …
HAR-GCNN: Deep graph CNNs for human activity recognition from highly unlabeled mobile sensor data
The problem of human activity recognition from mobile sensor data applies to multiple
domains, such as health monitoring, personal fitness, daily life logging, and senior care. A …
domains, such as health monitoring, personal fitness, daily life logging, and senior care. A …
Leveraging activity recognition to enable protective behavior detection in continuous data
Protective behavior exhibited by people with chronic pain (CP) during physical activities is
very informative to understanding their physical and emotional states. Existing automatic …
very informative to understanding their physical and emotional states. Existing automatic …