In the last few years the amount of research regarding gait analysis has increased considerably. The emergence of new technologies makes it possible to accurately measure a wide amount of gait aspects, detecting multiple physical disabilities and syndromes. In this sense, frailty syndrome describes elderly people who are dependent on others to perform basic needs. Obviously, the study and detection of frailty considers a set of parameters from different domains mainly from a clinical viewpoint. However, the functional domain has been classically appreciated as the independence level of a person, and the gait activity as the main predictor of functional disorders. Thus, gait analysis can be used in the detection and diagnosis of frailty. Development of mobile technologies and sensor devices facilitates the gait study, providing relevant information about the health condition of the person. Acquisition, segmentation, filtering, and depth analysis are the main stages in the treatment of the gait signal. This chapter presents a detailed study of gait activity from a quantitative viewpoint, taking into account multiple sensors and devices, as well as several analysis techniques. We study the use of accelerometer-enabled devices, pressure sensors, and cameras to collect gait data, which provides details according to our experience about the pros and cons of using specific and general purpose devices. Also, the estimation of gait parameters and the application of classification algorithms in gait analysis are discussed. We present three experimental systems that analyse gait parameters by using different devices and procedures and consider other relevant factors to support frailty detection. From the deployment of these systems, some results and conclusions are shown.

Chapter Contents:

• 10.1 Introduction
• 10.2 An overview of frailty syndrome: the importance of functional markers
• 10.3 Gait analysis procedure
• 10.3.1 Data acquisition: sources and fundamentals
• 10.3.2 Data segmentation and filtering
• 10.3.3 Data analysis: parameter estimation and identification of gait patterns
• 10.4 Overview of gait analysis systems and mechanisms
• 10.4.1 Specific purpose devices: sensors and tiny mechanisms
• 10.4.2 General purpose devices: smart and mobile devices
• 10.5 Gait analysis as part of a comprehensive study of frailty: experimental applications and case studies
• 10.5.1 Frailty detection and diagnosis system by using accelerometer-enabled smartphones and clinical information
• 10.5.1.1 Description
• 10.5.1.2 Results
• 10.5.2 Gait monitoring system based on wireless sensorised insoles
• 10.5.2.1 Description
• 10.5.2.2 Results
• 10.5.3 Computer vision system based on a structured light sensor
• 10.5.3.1 Description
• 10.5.3.2 Results
• 10.6 Conclusions
• Acknowledgements
• References

Inspec keywords: medical signal processing; pressure sensors; geriatrics; signal classification; patient diagnosis; gait analysis; accelerometers; biomedical equipment; medical disorders

Other keywords: accelerometer-enabled devices; cameras; pressure sensors; sensor devices; frailty diagnosis; quantitative techniques; depth analysis; syndromes; signal acquisition; gait activity; frailty detection; functional domain; gait signal treatment; human gait analysis; signal segmentation; signal filtering; mobile technology; functional disorders; classification algorithms

Subjects: Velocity, acceleration and rotation measurement; Signal processing and detection; Biology and medical computing; Pressure measurement; Patient diagnostic methods and instrumentation; Biomedical measurement and imaging; Physics of body movements; Pressure and vacuum measurement; Velocity, acceleration and rotation measurement; Digital signal processing