Functional range of motion in the metacarpophalangeal joints of the hand measured by single axis electric goniometers

Murai, Takashi; Uchiyama, Shigeharu; Nakamura, Kōichi; Ido, Yoshikazu; Hata, Yukihiko; Kato, Hiroyuki

doi:10.1016/j.jos.2018.01.013

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…Previous studies have shown that the thumb MCP joint can contribute up to 10 of extension during activities of daily living; however, baseline values of MCP joint motion are not obtained to correct for this. 20 We have shown that with a desktop electromagnetic motion sensor we can identify ROM differences in patients with thumb CMC arthritis compared with controls. The ability to obtain and monitor dynamic thumb ROM in an efficient manner can provide an objective measurement of disease progression.…”

Section: Discussionmentioning

confidence: 94%

Dynamic Thumb Circumduction Measured With a Wearable Motion Sensor: A Prospective Comparison of Patients With Basal Joint Arthritis to Controls

López

Gandhi

Falk

et al. 2021

Journal of Hand Surgery Global Online

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The purpose of this study was to compare the active range of motion in patients with thumb carpometacarpal (CMC) arthritis to healthy controls. A secondary objective of this study was to examine the feasibility of using wearable motion sensors in a clinical setting. Methods: Asymptomatic controls and patients with radiographic and clinical evidence of thumb CMC joint arthritis were recruited. The experimental setup allowed participants to rest their forearm in neutral pronosupination with immobilization of the second through fifth CMC joints. An electromagnetic motion sensor was embedded into a thumb interphalangeal joint immobilizer, and participants were asked to complete continuous thumb circumduction movements. Data were continuously recorded, and circumduction curves were created based on degrees of motion. Peak thumb abduction and extension angles were also extracted from the data. Results: A total of 29 extremities with thumb CMC arthritis and 18 asymptomatic extremities were analyzed. Bilateral disease was present in 64% of patients. Patient age range was 35e83 years, and the control group age range was 26e83 years. The most affected extremities had Eaton stage 3 disease (38%, N ¼ 11). The average maximum thumb abduction was 53.9 ± 19.6 in affected extremities and 70.8 ± 10.1 for controls. Average maximum thumb extension was 50.0 ± 15.2 in affected extremities and 58.4 ± 9.1 for controls. When comparing patients with Eaton stage 3 and 4 disease to controls, average maximum abduction and extension decreased with increasing disease stage (42.3 , 46.1 , and 70.8 for abduction, respectively, and 58.4 , 43.3 , and 41.3 for extension, respectively). Conclusions: We observed more severe motion limitations with increasing Eaton stage, and statistically significant differences were seen with stage 3 and 4 disease. A wearable motion sensor using a portable experimental setup was used to obtain measurements in a clinical setting. Type of study/level of evidence: Diagnostic II.

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Section: Discussionmentioning

confidence: 94%

Dynamic Thumb Circumduction Measured With a Wearable Motion Sensor: A Prospective Comparison of Patients With Basal Joint Arthritis to Controls

López

Gandhi

Falk

et al. 2021

Journal of Hand Surgery Global Online

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“…There is a large volume of research on the range of joint motion, including active range of motion (aROM) [ 10 , 11 , 12 , 13 , 14 , 15 ] and functional range of motion (fROM) which represent activities of daily living [ 7 , 11 , 12 , 15 , 16 ]. Depending on the joints and movement, fROM is 5° to 28° smaller than the available aROM [ 11 , 12 , 16 ]. The joint motion range corresponding to the execution process of the intentional gesture should be aROM.…”

Section: Introductionmentioning

confidence: 99%

Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression

Jia

Qin

Bai

et al. 2021

Sensors

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Continuous movements of the hand contain discrete expressions of meaning, forming a variety of semantic gestures. For example, it is generally considered that the bending of the finger includes three semantic states of bending, half bending, and straightening. However, there is still no research on the number of semantic states that can be conveyed by each movement primitive of the hand, especially the interval of each semantic state and the representative movement angle. To clarify these issues, we conducted experiments of perception and expression. Experiments 1 and 2 focused on perceivable semantic levels and boundaries of different motion primitive units from the perspective of visual semantic perception. Experiment 3 verified and optimized the segmentation results obtained above and further determined the typical motion values of each semantic state. Furthermore, in Experiment 4, the empirical application of the above semantic state segmentation was illustrated by using Leap Motion as an example. We ended up with the discrete gesture semantic expression space both in the real world and Leap Motion Digital World, containing the clearly defined number of semantic states of each hand motion primitive unit and boundaries and typical motion angle values of each state. Construction of this quantitative semantic expression will play a role in guiding and advancing research in the fields of gesture coding, gesture recognition, and gesture design.

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“…Therefore, in domains such as orthopedic surgery and physical therapy, the ROM is used as information for clinical reasoning, an index of surgical outcomes, and an index of the intervention effect 4,5) . Many methods for the measurement of joint ROM have been proposed, such as visual estimation 6) and the use of a universal goniometer 7) , electrogoniometer 8) , digital image 9) , or a threedimensional motion analyzing apparatus 10) . Additionally, a method using a smartphone has been recently developed 11,12) .…”

Section: Introductionmentioning

confidence: 99%

Extrinsic feedback from a feedback device promotes the learning of range of motion measurements

Akizuki

Mitamura²,

Yamamoto

et al. 2020

J Phys Ther Sci

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Although it is widely recognized that feedback is important for skill acquisition or improvement, feedback is not completely utilized in physical therapy education. Therefore, we aimed to verify the effect of extrinsic feedback from a feedback device on proficiency in range of motion measurements by a universal goniometer. [Participants and Methods] The participants included 22 physical therapy students who were randomly assigned to feedback (n=11) and non-feedback groups (n=11). The passive right knee flexion range of motion was set as the measurement task. The experiment consisted of a pretest phase, practice trials, and a posttest phase. In the pretest phase, all participants conducted three measurements without extrinsic feedback. Extrinsic feedback related to measurement error from a device was given only to the feedback group. The posttest was conducted 24 hours after the practice trials with the same content as that in the pretest. [Results] The improvement rate from pretest to posttest was greater in the feedback group than in the non-feedback group. The results indicated that the measurement error decreases with extrinsic measurement error-related feedback during practice. [Conclusion] The utilization of extrinsic feedback from a feedback device is effective for enhancing range of motion measurement skills.

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Functional range of motion in the metacarpophalangeal joints of the hand measured by single axis electric goniometers

Cited by 16 publications

References 16 publications

Dynamic Thumb Circumduction Measured With a Wearable Motion Sensor: A Prospective Comparison of Patients With Basal Joint Arthritis to Controls

Dynamic Thumb Circumduction Measured With a Wearable Motion Sensor: A Prospective Comparison of Patients With Basal Joint Arthritis to Controls

Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression

Extrinsic feedback from a feedback device promotes the learning of range of motion measurements

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