Accelerometer based human joints' range of movement measurement

Accurate measurement and analysis of joints' range of movement (ROM) are important for assessing joint related health conditions and are valuable to clinicians for diagnostic and rehabilitation purposes. As an alternative to using the camera-based methods which are restrictive and expensive, and the electro-goniometers which are not sufficiently effective in some scenarios, researchers are developing the use of microelectromechanical devices such as accelerometers for measuring human joints movement. This paper presents the development of an accelerometer based system to measure movement angle, velocity, acceleration and displacement for the knee

Accelerometry in sport

The possibilities to analyse various sports movements and activities, their techniques and dynamics are deepening and enhancing with the rapid development of modern technologies. It subsequently enables to improve and enhance sports techniques, performances and efficiency, to prevent possible injuries, to enhance training methods, and to reach sports results of higher quality. The use of accelerometry to observe, examine and analyse various movements and their techniques and dynamics in different human physical and sports activities has become of high prominence in several recent decades. The goal of the paper is to illustrate some possibilities of the use of accelerometry to analyse these aspects in the field of sport, in selected cyclic and acyclic sports. The study aims to outline and indicate a way of possible examination, observation and analysis of the data acquired by accelerometry measurements

Evaluation of three accelerometer devices for physical activity measurement amongst south Asians and Europeans

We recruited 62 South Asians and 40 Europeans aged 25 to 75 years, to assess the potential validity of three physical activity accelerometers for use amongst South Asians. Participants completed an exercise treadmill test (following Bruce protocol) while wearing the 3 accelerometers: Actigraph GT3X+ [GT3X+] and Geneactiv [GA] on ankle, waist and wrist; and Actiheart [AH] on chest. We compared relationships between energy expenditure (EE) measured by accelerometers (Measured) and actual EE on the treadmill (Actual) in the two ethnicities and tested for potential confounding effects. All accelerometers under-reported EE. Difference between Measured and Actual EE was smallest for GT3X+ankle (Measured – Actual at peak exercise [Mets]: GT3X+ankle –6.52 (1.77); GT3X+waist –8.46 (1.29); GT3X+wrist –11.17 (1.03); GAankle –8.17 (1.19); GAwaist –10.24 (0.64); GAwrist –11.21 (1.10); AHchest –9.09 (1.43), P 0.05). Relationship between Measured and Actual EE was not influenced by age, gender, height, waist, weight or waist-hip ratio (all P > 0.05). Amongst the devices and positions tested, GT3X+ankle is the most accurate device for measuring EE during an exercise treadmill test. Accelerometer performance is similar in South Asians and Europeans and is not influenced by anthropometric differences between the two populations

Using an inertial motion unit to find the optimal location to continuously monitor for near falls

The world population is ageing and a key hazard to healthy ageing is falls. The consequences of falls can be costly to health and social care systems. Falls can be prevented by continuously monitoring of older people for near falls, as they are a major risk factor for falls. This preliminary study’s aim was to find the optimal placement of a monitoring device to detect falls, as this is the first step towards understanding how to detect a near fall. This study involved one participant wearing four commercially available motion trackers simultaneously. The participant performed five controlled sideways falls onto a crash mat. The motion trackers were controlled using the associated software that also logged the data. The results presented display the accelerometer and gyroscope data for falls at the four locations (wrist, waist, ankle, and thigh). The data shows monitoring at the thigh gives the most consistent pattern per fall for both the accelerometer and gyroscope data

Assessing physical activity in older adults: Required days of trunk accelerometer measurements for reliable estimation

We investigated the reliability of physical activity monitoring based on trunk accelerometry in older adults and assessed the number of measured days required to reliably assess physical activity. Seventy-nine older adults (mean age 79.1 ± 7.9) wore an accelerometer at the lower back during two nonconsecutive weeks. The duration of locomotion, lying, sitting, standing and shuffling, movement intensity, the number of locomotion bouts and transitions to standing, and the median and maximum duration of locomotion were determined per day. Using data of week 2 as reference, intraclass correlations and smallest detectable differences were calculated over an increasing number of consecutive days from week 1. Reliability was good to excellent when whole weeks were assessed. Our results indicate that a minimum of two days of observation are required to obtain an ICC ≥ 0.7 for most activities, except for lying and median duration of locomotion bouts, which required up to five days

Human Action Recognition with RGB-D Sensors

none3noHuman action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.Cippitelli, Enea; Gambi, Ennio; Spinsante, SusannaCippitelli, Enea; Gambi, Ennio; Spinsante, Susann

Human Action Recognition with RGB-D Sensors

Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset