Our Ground Breaking Solution
The ActionSense team have developed a state of the art technology infused glove, which will analyse the movement of the hand, wrist and fingers, examining the flexibility and limitation in joint mobility of the patient’s hand through measurement of the maximum angular and velocity data.
Key features of the ActionSense glove include:
- Sensors placed on the fingers measuring linear movement;
- Sensor data is collected and returned to a control panel located on the wrist.
- Data is sent wirelessly to a remote application for analysis by the clinician.
This process allows the clinician to accurately diagnose the severity of the patient’s condition, prescribe treatments more efficiently and recommend rehabilitation plans, where appropriate.
How it Works
The ActionSense Data Glove provides detailed information on changes in the wearer’s joint mobility and flexibility. Clinicians can use this information for patient assessment and rehabilitation.
Comparison of completed exercise routines provides detailed movement analysis to the clinician on changes in joint mobility and flexibility.
An easy-to-use application provides informative and easily understandable feedback to the patient, and aids them to complete joint exercise routines at home. The goal of the ActionSense Data Glove is to motivate patient participation throughout their ambulatory rehabilitation, whilst providing detailed remote analysis of patient movement to the clinician.
The Software Behind the Technology
The ActionSense Motion Capture (MoCap) software provides tools to regulate glove functionality including sensor calibration, sensor recording and playback, and detailed analysis of recorded movement to measure and evaluate variance within exercise routines using statistical methods
The system is initialized for the patient in the clinic and is then subsequently used to record finger joint movement at the patents home. Customized exercise routines allow targeted measurement of specific finger joints and can be assigned to individual or groups of system users. The patient completes their first exercise routine at the clinic. This is defined as a baseline exercise routine and used for comparison against future completed objective routines.
Using the Software as a Predictive Tool
Intelligent techniques are applied in real-time to angular data throughout recording of patient movement. Each piece of sensor data is segmented into identified movement categories.
This data is presented using the MoCap analysis software. All timing information is displayed numerically and with colour coding. Colour coding each piece of data makes identification of movement changes easier to recognise and simplifies understanding of information. Statistical analytics are applied to each sub-section of movement to compare how segments within an exercise routine compare with other repetitions within the same exercise routine, or with other repetitions within different routines for the same patient.