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Knee Angle Analysis Using a Wearable Motion Analysis System for Detection and Rehabilitation of Mild Traumatic Brain Injury
Abstract
Mild traumatic brain injuries (mTBI) are common in soldiers and athletes, and can affect many areas of a person’s daily life including gait [1]. Current methods of measuring gait parameters involve expensive optical motion capture systems, time intensive setup, wires, complicated filtering techniques, and a laboratory setting. A wearable and wireless motion analysis system would allow gait analysis to be performed outside of a laboratory setting during activities of daily living, in a clinical setting or on a football field. The purpose of this study was to develop and verify an algorithm to calculate knee flexion during slow gait, particularly during terminal stance and pre-swing phases, using wireless wearable sensors.
Gait assessment is an essential tool for clinical applications not only to diagnose
different neurological conditions but also to monitor disease progression as it
contributes to the understanding of underlying deficits. There are established methods
and models for data collection and interpretation of gait assessment within different
pathologies. This narrative review aims to depict the evolution of gait assessment from
observation and rating scales to wearable sensors and laboratory technologies, and
provide possible future directions. In this context, we first present an extensive review
of current clinical outcomes and gait models. Then, we demonstrate commercially
available wearable technologies with their technical capabilities along with their use in
gait assessment studies for various neurological conditions. In the next sections, a
descriptive knowledge for existing inertial based algorithms and a sign based guide
that shows the outcomes of previous neurological gait assessment studies are
presented. Finally, we state a discussion for the use of wearables in gait assessment
and speculate the possible research directions by revealing the limitations and
knowledge gaps in the literature.
Wireless Sensor Networks continuously extend their practicability in real world applications and are passing their break-even to hit the consumer market. An increasing number of miniaturized wireless sensor nodes is acquirable, mostly intended to stream environmental data as well as tracking health with single node devices. A lack of distributed processing of sensor data within a cooperative network, a short lifetime or an extremely specialized design limit the use of wireless sensor nodes to specific applications. In this paper we target these drawbacks and introduce two consecutive hardware platforms - one platform for flexible development and a second miniaturized hardware platform for ambient integration in the environment. The goal is to enable applications for state-of-the-art Wireless Body Area Networks and Wireless Sensor Networks that fit the contradicting demands of a small form factor and durable network for distributed and cooperative processing. Furthermore, we focus with these platforms on optimization of energy consumption in order to extend the lifetime without maintenance. We support distributed movement evaluation with a multi-modal sensor technology for fine-grained motion tracking of a human body, for observing the structural health of buildings, for observing fences and borders or for any other versatile system.
Abstract Gait analysis is widely used in detecting human walking disorders. Current gait analysis methods like video- or optical-based systems are expensive and cause invasion of human privacy. This article presents a self-developed low-cost body inertial-sensing network, which contains a base station, three wearable inertial measurement nodes, and the affiliated wireless communication protocol, for practical gait discrimination between hemiplegia patients and asymptomatic subjects. Every sensing node contains one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope. Seven hemiplegia patients (all were abnormal on the right side) and 7 asymptomatic subjects were examined. The three measurement nodes were attached on the thigh, the shank, and the dorsum of the foot, respectively (all on the right side of the body). A new method, which does not need to obtain accurate positions of the sensors, was used to calculate angles of knee flexion/extension and foot in the gait cycle. The angle amplitudes of initial contact, toe off, and knee flexion/extension were extracted. The results showed that there were significant differences between the two groups in the three angle amplitudes examined (-0.52±0.98° versus 6.94±2.63°, 28.33±11.66° versus 47.34±7.90°, and 26.85±8.6° versus 50.91±6.60°, respectively). It was concluded that the body inertial-sensing network platform provided a practical approach for wearable biomotion acquisition and was effective for discriminating gait symptoms between hemiplegia and asymptomatic subjects.
Mild traumatic brain injuries (mTBI) stem from a number of causes such as illnesses, strokes, accidents or battlefield traumas. These injuries can cause issues with everyday tasks, such as gait, and are linked with vestibular dysfunction [1]. Current technology that measures gait parameters often requires time consuming set up and post processing and is limited to the laboratory setting. The purpose of this study was to develop a wearable motion analysis system (WMAS) using five commercially available inertial measurement units (IMU) working in unison to record and output four gait parameters in a clinically relevant way. The WMAS has the potential to be used to 1) help diagnose mTBI or other neurocognitive disorders; 2) provide feedback to a clinician during a training session; 3) collect gait parameter data outside of the laboratory setting to determine rehabilitation progress; 4) provide quantitative outcome measures for rehabilitation efficacy.
This paper presents a wearable system for long-term monitoring of knee kinematics in the home and community settings. The system consists of a knee sleeve with embedded sensors to track knee flexion/extension movements and to monitor compliance with the use of the knee sleeve. Knee flexion/extension movement data is captured using a potentiometer. Compliance with the use of the knee sleeve is monitored by using an e-textile sensor that measures the knee-sleeve fabric stretch thus allowing one to infer whether the subject wears the knee sleeve. The system is also equipped with a wireless unit that has the capability of relaying data to a smart phone. The smart phone can be used as a gateway to provide remote access to the data. Comparison of measures gathered using the proposed wearable system and a stereo-photogrammetric system for biomechanical data collection demonstrates the reliability of the knee kinematic data collected using the proposed knee sleeve.
Human motion capturing is used in ergonomics for ambulatory assessment of physical workloads in field. This is necessary to
investigate the risk of work-related musculoskeletal disorders. Since more than fifteen years the IFA is developing and using
the motion and force capture system CUELA, which is designed for whole-shift recordings and analysis of work-related postural
and mechanical loads. A modified CUELA system was developed based on 3D inertial measurement units to replace all mechanical
components in the present system. The unit consists of accelerometer and gyroscopes, measuring acceleration and angular rate
in three dimensions. The orientation determination based on angular rate has the risk of integration errors due to sensor
drift of the gyroscope. We introduced “zero points” to compensate gyroscope drift and reinitialize orientation computation.
In a first evaluation step the movements of lower extremities are analyzed and compared to the optical motion tracking system
Vicon.
To identify the most common gait abnormalities presenting after traumatic brain injury (TBI) and quantify their incidence rate.
Case series.
Biomechanics laboratory.
A convenience sample of 41 people with TBI receiving therapy for gait abnormalities, and a sample of 25 healthy controls.
Three-dimensional gait analysis.
Spatiotemporal, kinematic, and kinetic data at a self-selected walking speed.
People with TBI walked with a significantly slower speed than matched healthy controls. There was a significant difference between groups for cadence, step length, stance time on the affected leg, double support phase, and width of base of support. The most frequently observed biomechanical abnormality was excessive knee flexion at initial foot contact. Other significant gait abnormalities were increased trunk anterior/posterior amplitude of movement, increased anterior pelvic tilt, increased peak pelvic obliquity, reduced peak knee flexion at toe-off, and increased lateral center of mass displacement. Ankle equinovarus at foot-contact occurred infrequently.
People with TBI were found to have multijoint gait abnormalities. Many of these abnormalities have not been previously reported in this population.
Development of a wearable motion analysis system for evaluation and rehabilitation of mild traumatic brain injury (mTBI) Pearson's R Correlation Average Error (Degrees) Left Knee Angle Right Knee Angle Left Knee Angle Right Knee Angle
- S L Carey
Carey S.L., et al., 2012, " Development of a wearable motion analysis system for
evaluation and rehabilitation of mild traumatic brain injury (mTBI), " Proceedings of the
ASME 2012 Summer Bioengineering Conference (SBC2012), Fajardo, Puerto Rico, USA.
Pearson's R Correlation
Average Error (Degrees)
Left Knee
Angle
Right Knee
Angle
Left Knee
Angle
Right Knee
Angle
Subject 1
0.933
0.882
4.87
5.80
A wearable system for long-term monitoring of knee kinematics
- L D Toffola
- S Patel
- M Y Ozsecen
- R Ramachandran
- P Bonato
Toffola, L. D., Patel, S., Ozsecen, M. Y., Ramachandran, R., and Bonato, P., 2012, "A
wearable system for long-term monitoring of knee kinematics," 2012 IEEE-EMBS
International Conference on,Biomedical and Health Informatics (BHI), pp. 188-191.