Abstract
Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMU's movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A μSD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinson's disease symptoms, in gait analysis, and in a fall detection system.
Highlights
Recent technological developments in the automotive industry, telecommunications and electronics have enabled a major evolution in the possibilities of measuring and monitoring human movement [1].One of the main current interests is analyzing human motion in fields such as sports [2] and health [3].From the point of view of the field of dependency care, activity assessment and monitoring through human movement analysis is of great importance
In the studies performed in CETpD, the sensor is used at waist, which is considered the most suitable location for measuring human movement according to some authors [14,15,58]
These results show that the online classifier behavior has an autonomy of 41 h at 40 Hz, the highest autonomy after the datalog behavior without Bluetooth, which is 45 h
Summary
Recent technological developments in the automotive industry, telecommunications and electronics have enabled a major evolution in the possibilities of measuring and monitoring human movement [1]. The resulting supervised learning algorithms that determine symptoms or activities are referred in this paper as offline classifiers since they rely on previously collected data and they cannot provide real-time detection. Online classifiers are used in patients with drop foot syndrome to correct their gait in real-time through Functional Electrical Stimulation (FES) [31] In these different applications, online classifiers convert sensors signals into small pieces of relevant information whose storage requires less memory than the raw signals. This device, called 9 × 2, has been used in the clinical study in which PD symptoms were treated in real-time.
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