Pervasive Motion Tracking and Muscle Activity Monitor

Woodward, Richard B.; Shefelbine, Sandra J.; Vaidyanathan, Ravi

doi:10.1109/cbms.2014.43

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…The 10-50 Hz band pass filter applied to MMG measurements aims to remove the low-frequency bias and the high-frequency noise. These values have been selected as in Woodward et al, 13 since they represent the lower bound and the upper bound of the mean power frequency of MMG signals. 20 The 80 Hz lowpass filter applied to the torque measurements aims to remove high frequency noise.…”

Section: Signal Processingmentioning

confidence: 99%

“…Novel signal processing techniques have been developed, which include hardware and software filtering strategies, alongside feature ranking/selection algorithms to remove mechanical artefacts and isolate muscle activity in the MMG signal. 12,13 Following these technical advances in MMG hardware and software, their robustness in terms of validity and reliability needs to be examined. Standardised protocols can be followed for testing MMG signals against known measures of force and EMG during isometric and dynamic contractions to confirm known force/ MMG/EMG relationships.…”

Section: Introductionmentioning

confidence: 99%

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New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Meagher

Franco

Turk

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction: The Mechanical Muscle Activity with Real-time Kinematics project aims to develop a device incorporating wearable sensors for arm rehabilitation following stroke. These will record kinematic activity using inertial measurement units and mechanical muscle activity. The gold standard for measuring muscle activity is electromyography; however, mechanomyography offers an appropriate alterative for our home-based rehabilitation device. We have patent filed a new laboratory-tested device that combines an inertial measurement unit with mechanomyography. We report on the validity and reliability of the mechanomyography against electromyography sensors. Methods: In 18 healthy adults (27-82 years), mechanomyography and electromyography recordings were taken from the forearm flexor and extensor muscles during voluntary contractions. Isometric contractions were performed at different percentages of maximal force to examine the validity of mechanomyography. Root-mean-square of mechanomyography and electromyography was measured during 1 s epocs of isometric flexion and extension. Dynamic contractions were recorded during a tracking task on two days, one week apart, to examine reliability of muscle onset timing. Results: Reliability of mechanomyography onset was high (intraclass correlation coefficient ¼ 0.78) and was comparable with electromyography (intraclass correlation coefficient ¼ 0.79). The correlation between force and mechanomyography was high (R 2 ¼ 0.94). Conclusion: The mechanomyography device records valid and reliable signals of mechanical muscle activity on different days.

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Section: Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Meagher

Franco

Turk

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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“…The MMG implemented in this work followed the design of Woodward [19], seen in Figure 1. This sensor was fabricated based on the research of Posatski and Chou into chamber shapes [20].…”

Section: B Sensor Interface Systemmentioning

confidence: 99%

“…A Knowles' MEMS condenser microphone (SPU1410) is used as the sensor [16]. The housing of the sensor is 3D printed with the signal amplification cone sealed with a 4µm aluminum Mylar membrane [19]. In previous testing, when placed on the skin during the contraction of a lateral superficial muscle, low frequency vibrations were produced.…”

Section: B Sensor Interface Systemmentioning

confidence: 99%

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Rapid bicycle gear switching based on physiological cues

Hallett

Woodward

Schultz

et al. 2015

2015 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-This paper discusses the merits of Mechanomyography (MMG) sensors in capturing and isolating muscle activity in high interference environs, with application to 'hands free' gear shifting on a bicycle for users with limited extremity movement. MMG (acoustic) muscle sensing provides a simple and rugged alternative to physiological sensing for machine interface in the field, but suffers from interfering artifacts (in particular motion) which has limited its mainstream use. We introduce a system fusing MMG with a filter based on Inertial Measurement (IMU) to isolate muscle activity in the presence of interfering motion and vibrations. The system identifies user-initiated muscle trigger profiles during laboratory testing, allowing parameterization of MMG and IMU signals to identify purposeful muscle contractions (triggers) and to omit false triggers resulting from cycle/road vibration or rider movement. During laboratory testing the success rate of trigger identification was 88.5% while cycling with an average of 0.87 false triggers /min. During road testing the success rate was 72.5% and false triggers were more frequent at 3.7 /min. These results hold strong promise for alternative triggering mechanisms to the standard bar-end shifters used in current offthe-shelf cycling group sets, enabling amputees or people of reduced arm or hand dexterity to change gears while riding. Further testing will explore the use of signal filters on MMG data and further use of IMU data as feedback to increase false triggers rejection. Wider applications include a broad range of machineinteraction research.

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AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Suo,

Liu,

Wang

et al. 2024

Advanced Science

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Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.

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Pervasive Motion Tracking and Muscle Activity Monitor

Cited by 17 publications

References 14 publications

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Rapid bicycle gear switching based on physiological cues

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

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