Clip-On IMU System for Assessing Age-Related Changes in Hand Functions

Abstract

Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20–31) and sixteen healthy older adults (age 75–89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.

Deep Residual Networks for User Authentication via Hand-Object Manipulations

“…A clip-on wearable IMU-based system [ 27 ] was used in this study to capture users’ fine-motor finger and wrist joint movements while manipulating objects. The system comprises IMU-embedded hand and wrist modules that can be selectively applied to the fingers and wrist (see Figure 1 ).…”

“…To reduce possible misalignment between the IMU sensor and the earth frame, known as the drift phenomenon, the IMU-based wearable system embedded a gradient descent algorithm proposed by Madgwick et al [ 27 , 28 ]. To estimate realistic joint angle between a pair of IMU sensors, the equation proposed by Brennan et al [ 27 , 29 ] was used to correct joint estimation errors due to mounting the IMU sensors upon a subject’s hand. …”

“…The sampling rate of the embedded IMU sensor was 100 Hz. Further details of the specifications and mathematical descriptions are provided in our previous paper [ 27 ].…”

“…The tBBT is a standard hand dexterity test that aims to closely model common real-world object manipulation scenarios, in which a subject is required to control a movement from pick-up to release [ 27 ]. As shown in Figure 3 a, subjects were asked to transport a total of 16 blocks in a controlled manner, starting from grasping a block in a left box (Step 1–Step 2), transporting it over the partition (Step 2–Step 4), and releasing it in its mirrored position as quickly and accurately as possible (Step 4–Step 5).…”

“…Each module contained a 9-axis IMU sensor (a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer) to quantify the dynamic movements of the finger and wrist joints into discrete signals. Algorithms related to the IMU-based wearable system such as correcting misalignment and joint estimation error are based on our previous paper [27]. To reduce possible misalignment between the IMU sensor and the earth frame, known as the drift phenomenon, the IMU-based wearable system embedded a gradient descent algorithm proposed by Madgwick et al [27,28].…”