Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review

Weizman, Yehuda; Tirosh, Oren; Fuss, Franz Konstantin; Tan, Adin Ming; Rutz, Erich

doi:10.3390/s22051791

Cited by 15 publications

(15 citation statements)

References 44 publications

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“…Previous literature investigating the use of IMUs to assess spasticity has demonstrated promising results in terms of improving assessment reliability [ 3 , 11 , 15 , 17 , 25 ]. However, the clinical utility and ease of implementation for these previously reported systems is variable.…”

Section: Discussionmentioning

confidence: 99%

“…A newly published systematic review investigated the current use of inertia measuring unit (IMU) sensors to assess spasticity, in studies published between 2017 and 2021 [ 11 ]. The review highlights the promising nature of IMUs in this field whilst also identifying several gaps in the current literature requiring further investigation, including: (1) most of the studies have been completed in upper limb muscle groups, (2) the MAS remains the most commonly used clinical spasticity outcome measure, despite this assessment not including a velocity-dependent component, and (3) the MTS was not used in any of the studies included in the review despite the excellent agreement in classification of spasticity between the MTS and EMG [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale—A Proof of Concept Study

Banky

Williams

Davey

et al. 2022

Sensors

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Ankle spasticity is clinically assessed using goniometry to measure the angle of muscle reaction during the Modified Tardieu Scale (MTS). The precision of the goniometric method is questionable as the measured angle may not represent when the spastic muscle reaction occurred. This work proposes a method to accurately determine the angle of muscle reaction during the MTS assessment by measuring the maximum angular velocity and the corresponding ankle joint angle, using two affordable inertial sensors. Initially we identified the association between muscle onset and peak joint angular velocity using surface electromyography and an inertial sensor. The maximum foot angular velocity occurred 0.049 and 0.032 s following the spastic muscle reaction for Gastrocnemius and Soleus, respectively. Next, we explored the use of two affordable inertial sensors to identify the angle of muscle reaction using the peak ankle angular velocity. The angle of muscle reaction and the maximum dorsiflexion angle were significantly different for both Gastrocnemius and Soleus MTS tests (p = 0.028 and p = 0.009, respectively), indicating that the system is able to accurately detect a spastic muscle response before the end of the movement. This work successfully demonstrates how wearable technology can be used in a clinical setting to identify the onset of muscle spasticity and proposes a more accurate method that clinicians can use to measure the angle of muscle reaction during the MTS assessment. Furthermore, the proposed method may provide an opportunity to monitor the degree of spasticity where the direct help of experienced therapists is inaccessible, e.g., in rural or remote areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale—A Proof of Concept Study

Banky

Williams

Davey

et al. 2022

Sensors

Self Cite

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“…In many different industrial and technological fields, the above-mentioned data are becoming essential for diagnostic units, control systems and optimization algorithms. Some examples of such extensive expansions are self-driving vehicles, Unmanned Aerial Vehicles (UAV), wearable devices, robotic equipment and consumer electronics [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Reliability estimation of Inertial Measurement units using Accelerated Life Test

Carratù¹,

Catelani²,

Ciani³

et al. 2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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In many different technological and industrial fields microelectronic device reliability is rising up as a fundamental aspect to consider during the design of diagnostic, optimization and control systems. Unexpected failures in diagnostic and control units could lead to a severe impact on the entire system/plant availability. Thus, reliability analysis must be carried out during the early phase of the design. MEMS (Micro-Electro-Mechanical Systems) based Inertial Measurement Units are widespread in diagnostic units to monitor acceleration, position and angular velocity of machinery. However, recent literature lack of a reliability estimation for this kind of devices. Thus, this paper proposes a measurement setup and a customized Accelerated Life Test plan for reliability estimation of a set of Inertial Measurement Units. A temperature-based stress test based on the HTOL (High Temperature Operating Life) protocol have been carried out to age the devices with the aim of obtaining a failure dataset. Results of the test have been used to predict device's reliability.

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“…Among the wearable sensor approaches, a portable motion capture system with multiple inertial measurement units (IMUs) is gaining more interests recently [ 14 , 15 , 16 ]. The existing inertial motion capture systems in market, such as Xsens ® MVN system, require at least seventeen IMU modules/trackers (i.e., each IMU module/tracker needs to be placed on head, sternum, shoulders, upper arms, fore arms, hands, pelvis, upper legs, lower legs and feet) to construct a complete human motion model for comprehensive biomechanical analysis [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

Wang

Shan

et al. 2022

Sensors

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Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.

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Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review

Cited by 15 publications

References 44 publications

Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale—A Proof of Concept Study

Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale—A Proof of Concept Study

Reliability estimation of Inertial Measurement units using Accelerated Life Test

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

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