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Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.
It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.
Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.
Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks, or CNNs, have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.
In this tutorial, you will discover the ‘Activity Recognition Using Smartphones‘ dataset for time series classification and how to load and explore the dataset in order to make it ready for predictive modeling.
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.
It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.
Classical approaches to the problem involve hand crafting features from the time series data based on fixed-size windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.
Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks or CNNs have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.
InfluxData is for IoT deployments requiring support for thousands of sensors. Collect, store, visualize and alert on time-series data emitted from ARM, Arduino, Raspberry Pi and more.
Project Tango technology gives a mobile device the ability to navigate the physical world similar to how we do as humans. Project Tango brings a new kind of spatial perception to the Android device platform by adding advanced computer vision, image processing, and special vision sensors.
ION conference on inertial measurement systems, navigation and localization.
TeraRanger One is the most advanced distance sensor for robotics.
Its performance and lightweight design enable robotics applications that were previously impossible with slower sonar sensors or big heavy lasers. The sensor is fully eye-safe, pre-calibrated and ROS compatible. Simply attach it to your robot and you’re ready to go!
The sensor comes in two case designs but can also be customized in shape and colour for special applications. Please contact us for personalized solutions and configurations.
This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m × 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods.
At Sensors Expo today, Libelium announced the addition of ion selective sensor probes to the Waspmote Smart Water sensor platform, for increased sensitivity and accuracy in water quality monitoring.
The Waspmote Smart Water platform is an ultra low-power sensor node designed for use in rugged environments and deployment in Smart Cities in hard-to-access locations to detect changes and potential risk to public health in real time. Waspmote Smart Water is suitable for potable water monitoring, chemical leakage detection in rivers, remote measurement of swimming pools and spas, corrosion and limescale deposit, fish tank monitoring and seawater pollution levels.
Use the measrdroid app
- to collect interesting usage statistics.
- to access raw data from your mobile device.
Measured data will be used for a research project in computer science. They claim that all uploaded data samples are strictly kept private according to their privacy agreement. They do not collect social data.
MM7150
The Quartz Crystal Microbalance openQCM is a highly sensitive and fully open-source scientific device for applications in chemical and biological sensing. The core of openQCM is a quartz sensor capable of measuring mass deposition down to 1 billionth of grams. Arduino Inside.
FSRs are sensors that allow you to detect physical pressure, squeezing and weight. They are simple to use and low cost. This sensor is a Interlink model 406 FSR with a 38mm square sensing region. Note that this sensor can't detect where on the square you pressed (for that, check out their ribbon soft pots or capacitive touch pad).
I sensori a riflessione della serie LR-T rappresentano una sintesi perfetta fra tecnologia innovativa e robustezza funzionale.
Vision-Doctor.co.uk - Basics, knowledge, know-how, online tools and solutions for industrial machine vision applications - Lars Fermum
This is a list of sensors sorted by sensor type.