CYCLOPS: A cyclists' orientation data acquisition system using RGB camera and inertial measurement units (IMU)

This paper introduces CYCLOPS, an acquisition system developed to capture images and inertial measurement data of moving cyclists from a vehicle. The development of CYCLOPS addresses the need to acquire useful data for training machine learning models capable of predicting the motion intentions of cyclists on urban roads. Considering its application, it is a completely original development. The system consists of two devices. The first device is installed on the bicycle and is based on an electronic acquisition board comprising an inertial measurement unit (IMU), a microcontroller, and a transceiver for sending the cyclist's acceleration and orientation data to a vehicle. The second device is installed on the vehicle and uses the same board architecture to acquire the vehicle's accelerations and orientations, along with an RGB monocular camera. The data is stored in real-time in a laptop's drive for subsequent analysis and manipulation. The hardware architecture is presented in detail, including the designs to install the devices, for IMUs configuration, and software installation on the laptop. All design and software files required to develop the proposed system are available for download at: doi.org/10.17632/3yx5y8b7tm.1, licensed under the Open-source license CC BY 4.0.

Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data

Hip muscle weakness can be a precursor to or a result of lower limb injuries. Assessment of hip muscle strength and muscle motor fatigue in the clinic is important for diagnosing and treating hip-related impairments. Muscle motor fatigue can be assessed with surface electromyography (sEMG), however sEMG requires specialized equipment and training. Inertial measurement units (IMUs) are wearable devices used to measure human motion, yet it remains unclear if they can be used as a low-cost alternative method to measure hip muscle fatigue. The goals of this work were to (1) identify which of five pre-selected exercises most consistently and effectively elicited muscle fatigue in the gluteus maximus, gluteus medius, and rectus femoris muscles and (2) determine the relationship between muscle fatigue using sEMG sensors and knee wobble using an IMU device. This work suggests that a wall sit and single leg knee raise activity fatigue the gluteus medius, gluteus maximus, and rectus femoris muscles most reliably (p < 0.05) and that the gluteus medius and gluteus maximus muscles were fatigued to a greater extent than the rectus femoris (p = 0.031 and p = 0.0023, respectively). Additionally, while acceleration data from a single IMU placed on the knee suggested that more knee wobble may be an indicator of muscle fatigue, this single IMU is not capable of reliably assessing fatigue level. These results suggest the wall sit activity could be used as simple, static exercise to elicit hip muscle fatigue in the clinic, and that assessment of knee wobble in addition to other IMU measures could potentially be used to infer muscle fatigue under controlled conditions. Future work examining the relationship between IMU data, muscle fatigue, and multi-limb dynamics should be explored to develop an accessible, low-cost, fast and standardized method to measure fatiguability of the hip muscles in the clinic.

Metal-backed or all-poly tibial components: which are better for medial unicompartmental knee arthroplasty? A propensity-score-matching retrospective study at the 5-year follow-up

BACKGROUND

This retrospective medium-term follow-up study compares the outcomes of medial fixed-bearing unicompartmental knee arthroplasty (mUKA) using a cemented metal-backed (MB) or an all-polyethylene (AP) tibial component.

MATERIALS AND METHODS

The database of our institution was mined for primary mUKA patients implanted with an MB or an AP tibial component (the MB-UKA and AP-UKA groups, respectively) from 2015 to 2018. We compared patient demographics, patient-reported outcome measures (PROMs), and motion analysis data obtained with the Riablo™ system (CoRehab, Trento, Italy). We conducted propensity-score-matching (PSM) analysis (1:1) using multiple variables.

RESULTS

PSM analysis yielded 77 pairs of MB-UKA and AP-UKA patients. At 5 years, the physical component summary (PCS) score was 52.4 ± 8.3 in MB-UKA and 48.2 ± 8.3 in AP-UKA patients (p < 0.001). The Forgotten Joint Score (FJS-12) was 82.9 ± 18.8 in MB-UKAs and 73.4 ± 22.5 in AP-UKAs (p = 0.015). Tibial pain was reported by 7.8% of the MB-UKA and 35.1% of the AP-UKA patients (p < 0.001). Static postural sway was, respectively, 3.9 ± 2.1 cm and 5.4 ± 2.3 (p = 0.0002), and gait symmetry was, respectively, 92.7% ± 3.7 cm and 90.4% ± 5.4 cm (p = 0.006). Patient satisfaction was 9.2 ± 0.8 in the MB-UKA and 8.3 ± 2.0 in the AP-UKA group (p < 0.003).

CONCLUSIONS

MB-UKA patients experienced significantly better 5-year static sway and gait symmetry outcomes than AP-UKA patients. Although the PROMs of the two groups overlapped, MB-UKA patients had a lower incidence of tibial pain, better FJS-12 and PCS scores, and were more satisfied.

Estimation of lower back muscle force in a lifting task using wearable IMUs

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios.

Disentangling Cerebellar and Parietal Contributions to Gait and Body Schema: A Repetitive Transcranial Magnetic Stimulation Study

The overlap between motor and cognitive signs resulting from posterior parietal cortex (PPC) and cerebellar lesions can mask their relative contribution in the sensorimotor integration process. This study aimed to identify distinguishing motor and cognitive features to disentangle PPC and cerebellar involvement in two sensorimotor-related functions: gait and body schema representation. Thirty healthy volunteers were enrolled and randomly assigned to PPC or cerebellar stimulation. Sham stimulation and 1 Hz-repetitive-Transcranial-Magnetic-Stimulation were delivered over P3 or cerebellum before a balance and a walking distance estimation task. Each trial was repeated with eyes open (EO) and closed (EC). Eight inertial measurement units recorded spatiotemporal and kinematic variables of gait. Instability increased in both groups after real stimulation: PPC inhibition resulted in increased instability in EC conditions, as evidenced by increased ellipse area and range of movement in medio-lateral and anterior-posterior (ROMap) directions. Cerebellar inhibition affected both EC (increased ROMap) and EO stability (greater displacement of the center of mass). Inhibitory stimulation (EC vs. EO) affected also gait spatiotemporal variability, with a high variability of ankle and knee angles plus different patterns in the two groups (cerebellar vs parietal). Lastly, PPC group overestimates distances after real stimulation (EC condition) compared to the cerebellar group. Stability, gait variability, and distance estimation parameters may be useful clinical parameters to disentangle cerebellar and PPC sensorimotor integration deficits. Clinical differential diagnosis efficiency can benefit from this methodological approach.

From theory to practice: Monitoring mechanical power output during wheelchair field and court sports using inertial measurement units

An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9-1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.

Accuracy of unilateral and bilateral gait assessment using a mobile gait analysis system at different walking speeds

BACKGROUND

Previous research on the accuracy of mobile measurement systems has focused on parameters related to the whole gait cycle. Specifically, bilateral gait characteristics were primarily used as outcome measures.

RESEARCH QUESTION

How accurate are unilateral gait characteristics detected using a mobile system at various fixed walking speeds?

METHODS

Gait analysis during treadmill walking at velocities (VEL) of 2.5 (v1), 4.5 (v2) and 6.5 km/h (v3) was performed in a population of 47 healthy young adults, consisting of 27 females (age: 23 ± 2 years, BMI: 21.4 ± 2.2 kg/m²) and 20 males (age: 22 ± 1 years, BMI: 23.3 ± 3.4 kg/m²). Spatiotemporal gait data were simultaneously determined using an instrumented treadmill (gaitway 3D) and a mobile gait analysis system (RehaGait). Besides VEL, bilateral (stride length [SL], cadence [CAD]) and unilateral (contact duration [CON], single [SS] and double support duration [DS]) outcomes were validated.

RESULTS

Across the three VEL investigated, the correlations between both measurement systems were almost perfect in SL and CAD (r > 0.97). In addition, SL significantly differed (p < 0.01) with moderate to large effects, whereby the root mean squared error (RMSE) did not exceed 1.8 cm. RMSE in CAD was not higher than 0.33 spm and statistically significant differences were only present at v1 (d = 0.63). DS was the most erroneous unilateral parameter with values for %RMSE ranging from 9% at v1 to 14% at v3. In CON and SS %RMSE was in a magnitude of 2-4% across all VEL. Furthermore, VEL affected measurement accuracy in unilateral outcomes with moderate to large effects (F (2, 45) > 6.0, p < 0.01, ηp2 > 0.11) with consistently higher differences at lower velocities.

SIGNIFICANCE

Based on the results presented the validity of the mobile gait analysis system investigated to detect gait asymmetries must be questioned.

Gait kinematics based on inertial measurement units with the sensor-to-segment calibration and multibody optimization adapted to the patient's motor capacities, a pilot study

INTRODUCTION

Inertial Measurement Units (IMUs) offer a promising alternative to optoelectronic systems to obtain joint lower-limb kinematics during gait. However, the associated methodologies, such as sensor-to-segment (S2S) calibration and multibody optimization, have been developed mainly for, and tested on, asymptomatic subjects.

RESEARCH QUESTION

This study proposes to evaluate two personalizations of the methodology used to obtain lower-body kinematics from IMUs with pathological subjects: S2S calibration and multibody optimization.

METHODS

Based on previous studies, two decision trees were developed to select the best (in terms of accuracy and repeatability) S2S methods to be performed by the patient given his/her abilities. The multibody optimization was personalized by limiting the kinematic chain range of motion to the results of the subject's clinical examination. These two propositions were tested on 12 patients with various gait deficits. The patients were equipped with IMUs and reflective markers tracked by an optoelectronic system. They had to perform the postures and movements selected by the decision trees then walk back and forth along a walkway. Gait kinematics obtained from the IMUs directly (referred to as Direct kinematics), and after multibody optimization performed via the OpenSim software using the generic range of motion (referred to as Generic Optimized kinematics), and using the personalized range of motion (referred to as Personalized Optimized kinematics) were compared to those obtained with the Conventional Gait Model through Root Mean Square Errors (RMSE), Correlation Coefficients (CC) and Range of Motion differences (ΔROM).

RESULTS

The RMSEs were smaller than 8.1° in the sagittal plane but greater than 7.4° in the transverse plane. The CCs, between 0.71 and 0.99 in the sagittal plane, deteriorate sharply in the frontal and transverse planes where they only measured between 0.15 and 0.68. The ΔROMs were mostly below 8.3°. Optimized kinematics did not improve compared to Direct kinematics.

SIGNIFICANCE

The personalization of the proposed S2S calibration method showed encouraging results, whereas multibody optimization did not impact the resulting joint kinematics.

Turning and sitting movements during timed up and go tests predict deterioration of physical function in middle-aged adults

BACKGROUND

Deterioration of physical function in middle-aged adults is a significant challenge that can lead to increased risk of future falls. However, a screening method for the functional decline in middle-aged adults has not been established.

RESEARCH QUESTION

To evaluate the relationship between biomechanical parameters assessed by the timed up and go test (TUG) and locomotive syndrome (LS) in middle-aged adults.

METHODS

The inclusion criterion was: under 65 years of age. A total of 97 volunteers (mean age 51.1 years) participated in this study. An LS test was performed, including a 2-step test, a stand-up test, and a 25-question Geriatric Locomotive Function Scale. The TUG was measured using inertial measurement units (IMUs) at comfortable and fast speeds. We then determined the minimum values for anterior-posterior acceleration and angular velocity around the medial-lateral axis, as well as the maximum values of angular velocity around the vertical axis for the upper trunk and sacrum in a TUG phase.

RESULTS

Angular velocity around the vertical axis for upper trunk and sacrum were significantly smaller in LS than non-LS in the turn phase of both speed conditions. For the fast speed condition, the minimum anterior-posterior acceleration for sacrum was greater in LS than in the non-LS condition for the stand-to-sit phase. Angular velocity around the vertical axis for turning and anterior-posterior acceleration from sitting were associated with detection of LS.

SIGNIFICANCE

Turning and sitting movements during TUG should be observed using IMU to screen for physical function decline in middle aged adults.

Relationship between coordination variability and Osgood-Schlatter disease in male junior youth soccer players -cross-sectional study using an inertial measurement unit

BACKGROUND

Osgood-Schlatter disease is a common overuse injury, and motor coordination is discussed as a risk factor; however, no reports have examined motor coordination in young soccer players with Osgood-Schlatter disease. This study aimed to investigate the difference in motor coordination between Osgood-Schlatter disease-affected and non-affected soccer players on a junior youth soccer team.

METHODS

This cross-sectional study investigated 35 young soccer players of 12-15 years of age, who completed a self-administered questionnaire covering general information, injury history, and athletic experience. An inertial measurement unit was attached to the participant's thoracic spine, lumbar spine, pelvis, thigh, and lower leg. The sagittal plane tilt angle of each body segment during squatting was analyzed. The continuous relative phase was calculated using the sagittal plane tilt angle. The mean absolute relative phase and continuous relative phase variabilities were calculated and compared between Osgood-Schlatter disease-affected and non-affected players.

FINDINGS

The sagittal plane tilt angle of each body segment during static standing and maximum flexion did not differ between the two groups. However, the Osgood-Schlatter disease group had significantly less continuous relative phase variability between the lumbar spine and pelvis (P < 0.01, Cohen's d = 0.91). The Osgood-Schlatter disease group had significantly fewer participants with other sports experience (P = 0.032, φ = 0.36).

INTERPRETATION

Dysfunctional lower trunk and hip muscles may be leading to Osgood-Schlatter disease. It is suggested that a variety of physical activities should be performed in the junior age group to allow players to acquire a variety of movement patterns.

Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters

BACKGROUND

Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults.

METHODS

Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. The logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals. Furthermore, we conducted an ablation experiment to explore the complementary information offered by the two sensors.

RESULTS

Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Some of these parameters, such as the usage of walking assistance, maximum angular velocity around the yaw axis during turn-to-sit, and step length, exhibit the greatest discriminatory abilities in identifying high-fall-risk individuals. Additionally, combining features from both devices for fall risk assessment resulted in a higher AUC of 0.882 compared to using each device separately.

CONCLUSIONS

Utilizing different types of sensors can offer more comprehensive information. Interpreting parameters to physiology provides deeper insights into the identification of high-fall-risk individuals. High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as less knee flexion and a tendency to tilt the pelvis forward during turning.

Differences in kinematic parameters during gait between the patients with knee osteoarthritis and healthy controls using an insole with a single inertial measurement unit: A case-control study

BACKGROUND

An inertial measurement unit is small and lightweight, allowing patient measurements without physical constraints. This study aimed to determine the differences in kinematic parameters during gait using an insole with a single inertial measurement unit in healthy controls and on both sides in patients with knee osteoarthritis.

METHODS

Twenty patients with knee osteoarthritis and 13 age-matched controls were included in this study. The participants walked at a self-selected speed and foot kinematics were measured during gait using an insole with a single inertial measurement unit. The right side of the healthy controls and both the affected and contralateral sides of patients with KOA were analyzed separately.

FINDINGS

The foot extension angular velocity at toe-off was significantly reduced on the affected side than on the contralateral side (P < 0.001) and in healthy controls (P < 0.001). During the swing phase, foot posterior-anterior acceleration was significantly lower on the affected side than on the healthy controls (P = 0.005). Furthermore, despite a decrease in walking speed, foot superior-inferior acceleration at initial contact in patients was significantly lower on the contralateral side than in healthy controls (P = 0.0167), but not on the affected side (P = 0.344).

INTERPRETATION

An insole with a single inertial measurement unit can detect differences in foot kinematics during gait between healthy controls and patients with knee osteoarthritis. Our findings indicate that patients with knee osteoarthritis exhibit dysfunction of push-off at toe-off and shock absorption at initial contact on the affected side.

Beyond timing and step counting in 360° turning-in-place assessment: a scoping review

BACKGROUND

Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed.

METHODS

Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated.

RESULTS

Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation.

CONCLUSION

This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy.

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation

Objective

Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation.

Methods

We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing.

Results

When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%.

Conclusion

SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data.

Significance

This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

Toward Personalized Orthopedic Care: Validation of a Smart Knee Brace

Introduction

Wearable technology offers a promising solution to advance current rehabilitation strategies for post-operative orthopedic care. The aim of this study was to determine the level of agreement and concurrent validity of a smart knee brace compared to the gold standard measurement system GAITRite® for assessing lower limb gait parameters.

Methods

Thirty-four healthy participants were fitted with the smart knee brace (Digital Knee®) on their dominant limb. Gait parameters (stride length, stride time, and gait velocity) were measured simultaneously using the Digital Knee® and the GAITRite® electronic walkway. Two walks were performed at a comfortable speed and two at a fast-walking speed.

Results

At a comfortable walking speed, stride time was moderately valid (ICC2,1 = 0.66 s), and stride length and gait velocity demonstrated poor validity (ICC2,1 = 0.29; ICC2,1 = 0.41). All gait parameters demonstrated poor validity at a fast-walking speed (ICC2,1 = -0.16 to -0.01). Bias ranged from -0.08 to 0.28, with more clinically acceptable percentage errors at a comfortable walking speed (14.1-30%) versus at a fast-walking speed (26.4-42.6%). Gait velocity and stride length had substantially higher biases in the fast-walking speed compared to the comfortable walking speed (0.28 ± 0.39 m s-1 vs. 0.02 ± 0.21 m s-1; 0.15 ± 0.23 m vs. -0.04 ± 0.17 m). Limits of agreement were considered narrower for stride time compared to stride length and gait velocity.

Conclusion

The Digital Knee® is a promising approach to improving post-operative rehabilitation outcomes in patients with osteoarthritis. The Digital Knee® demonstrated good agreement and moderate concurrent validity for measuring gait metrics at a comfortable walking speed. These findings highlight the opportunity of the wearable sensor as an intervention for post-operative orthopedic care. This was a laboratory-based study; thus, further research is required to validate the wearable sensor in real-world contexts and in patients with knee pathologies. Further, refinement of the algorithm for measuring gait metrics at slow- and fast-walking speed with the Digital Knee® is warranted.

Validation of algorithms for calculating spatiotemporal gait parameters during continuous turning using lumbar and foot mounted inertial measurement units

Spatiotemporal gait parameters such as step time and walking speed can be used to quantify gait performance and determine physical function. Inertial measurement units (IMUs) allow for the measurement of spatiotemporal gait parameters in unconstrained environments but must be validated against a gold standard. While many IMU systems and algorithms have been validated during treadmill walking and overground walking in a straight line, fewer studies have validated algorithms during more complex walking conditions such as continuous turning in different directions. This study explored the concurrent validity in a population of healthy adults (range 26-52 years) of three different algorithms using lumbar and foot mounted IMUs to calculate spatiotemporal gait parameters: two methods utilizing an inverted pendulum model, and one method based on strapdown integration. IMU data was compared to a Vicon twelve-camera optoelectronic system, using data collected from 9 participants performing straight walking and continuous walking trials at different speeds, resulting in 162 walking trials in total. Intraclass correlation coefficients (ICCA,1) for absolute agreement were calculated between the algorithm outputs and Vicon output. Temporal parameters were comparable in all methods and ranged from moderate to excellent, except double support time which was poor. Strapdown integration performed better for estimating spatial parameters than pendulum models during straight walking, but worse during turning. Selecting the most appropriate model should take into consideration both speed and walking condition.

Trunk motion influences mechanical power estimates during wheelchair propulsion

In wheelchair sports, there is an increasing need to monitor mechanical power in the field. When rolling resistance is known, inertial measurement units (IMUs) can be used to determine mechanical power. However, upper body (i.e., trunk) motion affects the mass distribution between the small front and large rear wheels, thus affecting rolling resistance. Therefore, drag tests - which are commonly used to estimate rolling resistance - may not be valid. The aim of this study was to investigate the influence of trunk motion on mechanical power estimates in hand-rim wheelchair propulsion by comparing instantaneous resistance-based power loss with drag test-based power loss. Experiments were performed with no, moderate and full trunk motion during wheelchair propulsion. During these experiments, power loss was determined based on 1) the instantaneous rolling resistance and 2) based on the rolling resistance determined from drag tests (thus neglecting the effects of trunk motion). Results showed that power loss values of the two methods were similar when no trunk motion was present (mean difference [MD] of 0.6 ± 1.6 %). However, drag test-based power loss was underestimated up to -3.3 ± 2.3 % MD when the extent of trunk motion increased (r = 0.85). To conclude, during wheelchair propulsion with active trunk motion, neglecting the effects of trunk motion leads to an underestimated mechanical power of 1 to 6 % when it is estimated with drag test values. Depending on the required accuracy and the amount of trunk motion in the target group, the influence of trunk motion on power estimates should be corrected for.

Evaluation of the Vmaxpro sensor for assessing movement velocity and load-velocity variables: accuracy and implications for practical use

We investigated the ecological validity of an inertial measurement unit (IMU) (Vmaxpro) to assess the movement velocity (MV) during a 1-repetition maximum (1RM) test and for the prediction of load-velocity (L-V) variables, as well as the ecological intra- day and inter-day reliability during free-weight bench press (BP) and squat (SQ). Furthermore, we provide recommendations for the practical use of the sensor. Twenty-three strength-trained men completed an incremental 1RM test, whereas seventeen men further participated in another 3 sessions consisting of 3 repetitions with 4 different loads (30, 50, 70 and 90% of 1RM) to assess validity and intra- and inter-day reliability, respectively. The MV was assessed using the Vmaxpro and a 3D motion capture system (MoCap). L-V variables and the 1RM were calculated based on submaximal velocities. The Vmaxpro showed high validity during the 1RM test for BP (r = 0.935) and SQ (r = 0.900), but with decreasing validity at lower MVs. The L-V variables and the 1RM demonstrated high validity for BP (r = 0.808-0.942) and SQ (r = 0.615-0.741) with a systematic overestimation. Coefficients of variance for intra- and inter-day reliability ranged from 2.4% to 9.7% and from 3.2% to 8.6% for BP and SQ, respectively. The Vmaxpro appears valid at high and moderately valid at low MVs. Depending on the required degree of accuracy, the sensor may be sufficient for the prediction of L-V variables and the 1RM. Our data indicate the sensor to be suitable for monitoring changes in MVs within and between training sessions.

Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals

Background

Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS).

Methods

Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition.

Results

For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants.

Conclusions

Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.

Exposure to inclined trunk postures in surgical staff

In surgical staff, low-back pain (LBP) is prevalent and prolonged trunk inclination is hypothesized to be one of its potential causes. The aim of this study was to evaluate the magnitude and duration of trunk inclination in the sagittal plane of surgical assistants during surgical procedures. The three-dimensional trunk orientation was measured in 91 surgical assistants across four medical facilities during surgical procedures using an inertial measurement unit on the thorax. Per participant, Exposure Variation Analysis was used to evaluate the percentage of the total time of trunk inclination (< -10° (backward inclination); -10-10° (upright posture); 10-20° (light inclination); 20-30° (moderate inclination); >30° (strong inclination)) taking into account posture duration (< 10 s; 10-60 s; 60-300 s; > 300 s). Participants reported their LBP history and perceived low-back load during the procedure via a questionnaire. Participants were in an upright posture for 75% [63-84%] (median [interquartile range]) of the total surgery time (average surgery time: 174 min). Trunk inclination was beyond 20° and 30° for 4.3% [2.1-8.7%] and 1.5% [0.5-3.2%] of the surgery time, respectively. In most of the participants, the duration of trunk inclination beyond 20° or 30° was less than 60 s. Questionnaire response rate was 81%. Persistent or repeated LBP was reported by 49% of respondents, and was unrelated to the exposure to inclined trunk postures. It is concluded that other factors than prolonged trunk inclination, for instance handling of loads or prolonged standing may be causally related to the reported LBP in the investigated population.

Applications of wearable sensors in upper extremity MSK conditions: a scoping review

PURPOSE

This scoping review uniquely aims to map the current state of the literature on the applications of wearable sensors in people with or at risk of developing upper extremity musculoskeletal (UE-MSK) conditions, considering that MSK conditions or disorders have the highest rate of prevalence among other types of conditions or disorders that contribute to the need for rehabilitation services.

MATERIALS AND METHODS

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) extension for scoping reviews guideline was followed in this scoping review. Two independent authors conducted a systematic search of four databases, including PubMed, Embase, Scopus, and IEEEXplore. We included studies that have applied wearable sensors on people with or at risk of developing UE-MSK condition published after 2010. We extracted study designs, aims, number of participants, sensor placement locations, sensor types, and number, and outcome(s) of interest from the included studies. The overall findings of our scoping review are presented in tables and diagrams to map an overview of the existing applications.

RESULTS

The final review encompassed 80 studies categorized into clinical population (31 studies), workers' population (31 studies), and general wearable design/performance studies (18 studies). Most were observational, with 2 RCTs in workers' studies. Clinical studies focused on UE-MSK conditions like rotator cuff tear and arthritis. Workers' studies involved industrial workers, surgeons, farmers, and at-risk healthy individuals. Wearable sensors were utilized for objective motion assessment, home-based rehabilitation monitoring, daily activity recording, physical risk characterization, and ergonomic assessments. IMU sensors were prevalent in designs (84%), with a minority including sEMG sensors (16%). Assessment applications dominated (80%), while treatment-focused studies constituted 20%. Home-based applicability was noted in 21% of the studies.

CONCLUSION

Wearable sensor technologies have been increasingly applied to the health care field. These applications include clinical assessments, home-based treatments of MSK disorders, and monitoring of workers' population in non-standardized areas such as work environments. Assessment-focused studies predominate over treatment studies. Additionally, wearable sensor designs predominantly use IMU sensors, with a subset of studies incorporating sEMG and other sensor types in wearable platforms to capture muscle activity and inertial data for the assessment or rehabilitation of MSK conditions.

Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning

Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units

Human action intent recognition has become increasingly dependent on computational accuracy, real-time responsiveness, and model lightness. Model selection, data filtering, and experimental design are three critical factors for the recognition of human intention in research. However, the performance of machine learning algorithms can vary depending on factors such as sensor location, the number of sensors used, channel selection, and dimensional combinations. Moreover, the collection of adequate and balanced data in such scenarios can be challenging. To address this issue, we present a comparative analysis of 12 commonly used machine learning algorithms for human action intention recognition. The synthetic minority oversampling technique is applied to fill in missing data. Traversing all possible combinations would require conducting 686 experiments, which is a daunting task in terms of both cost and efficiency. To tackle this challenge, we employ an orthogonal experiment design based on the Quasi-horizontal method. Our analysis indicates that lightGBM outperforms other algorithms in recognizing eight human daily activities. Furthermore, we conduct a polar difference and variance analysis based on a comprehensive balanced multi-metric orthogonal experiment for lightGBM using various sensor combinations and dimensions. The optimal combinations of different sensor numbers in terms of position, channel, and dimension are derived using this approach. Notably, our experimental design reduces the number of experiments required to only 49 times.

Extraction of canine gait characteristics using a mobile gait analysis system based on inertial measurement units

This study aims to investigate two simple algorithms for extracting gait features from an inertial measurement unit (IMU) based canine gait analysis system. The first algorithm was developed to determine the hip/shoulder extension/flexion range of motion. The second algorithm automatically determines the stance and swing phase per leg. To investigate the accuracy of the algorithms, two dogs were walked on a treadmill and measured simultaneously with an IMU system, an optical tracking system and two cameras. The range of motion estimation was compared to the optical tracking systems, with a total of 280 steps recorded. To test the stance and swing phase detection, a total of 63 steps were manually annotated in the video recordings and compared with the output of the algorithm. The IMU's-based estimation of the range of motion showed an average deviation of 1.4° to 5.6° from the optical reference, while the average deviation in the detection of the beginning and end of the stance and swing phases ranged from -0.01 to 0.09 s. This study shows that even simple algorithms can extract relevant information from inertial measurements that are comparable to results from more complex approaches. However, additional studies including a wider subject pool need to be conducted to investigate the significance of the presented findings.

Subthalamic 85 Hz deep brain stimulation improves walking pace and stride length in Parkinson's disease patients

BACKGROUND

Mobile gait sensors represent a compelling tool to objectify the severity of symptoms in patients with idiopathic Parkinson's disease (iPD), but also to determine the therapeutic benefit of interventions. In particular, parameters of Deep Brain stimulation (DBS) with its short latency could be accurately assessed using sensor data. This study aimed at gaining insight into gait changes due to different DBS parameters in patients with subthalamic nucleus (STN) DBS.

METHODS

An analysis of various gait examinations was performed on 23 of the initially enrolled 27 iPD patients with chronic STN DBS. Stimulation settings were previously adjusted for either amplitude, frequency, or pulse width in a randomised order. A linear mixed effects model was used to analyse changes in gait speed, stride length, and maximum sensor lift.

RESULTS

The findings of our study indicate significant improvements in gait speed, stride length, and leg lift measurable with mobile gait sensors under different DBS parameter variations. Notably, we observed positive results at 85 Hz, which proved to be more effective than often applied higher frequencies and that these improvements were traceable across almost all conditions. While pulse widths did produce some improvements in leg lift, they were less well tolerated and had inconsistent effects on some of the gait parameters. Our research suggests that using lower frequencies of DBS may offer a more tolerable and effective approach to enhancing gait in individuals with iPD.

CONCLUSIONS

Our results advocate for lower stimulation frequencies for patients who report gait difficulties, especially those who can adapt their DBS settings remotely. They also show that mobile gait sensors could be incorporated into clinical practice in the near future.

Concurrent validity and between-unit reliability of a foot-mounted inertial measurement unit to measure velocity during team sport activity

The concurrent validity and between-unit reliability of a foot-mounted inertial measurement unit (F-IMU) was investigated during linear and change of direction running drills. Sixteen individuals performed four repetitions of two drills (maximal acceleration and flying 10 m sprint) and five repetitions of a multi-directional movement protocol. Participants wore two F-IMUs (Playermaker) and 10 retro-reflective markers to allow for comparisons to the criterion system (Qualisys). Validity of the F-IMU derived velocity was assessed via root-mean-square error (RMSE), 95% limits of agreement (LoA) and mean difference with 95% confidence interval (CI). Between-unit reliability was assessed via intraclass correlation (ICC) with 90% CI and 95% LoA. The mean difference for instantaneous velocity for all participants and drills combined was -0.048 ± 0.581 m ∙ s^-1, the LoA were from -1.09 to -1.186 m ∙ s^-1 and RMSE was 0.583 m ∙ s^-1. The ICC ranged from 0.84 to 1, with LoA from -7.412 to 2.924 m ∙ s^-1. Differences were dependent on the reference speed, with the greatest absolute difference (-0.66 m ∙ s^-1) found at velocities above 7 m ∙ s^-1. Between-unit reliability of the F-IMU ranges from good to excellent for all locomotor characteristics. Playermaker has good agreement with 3D motion capture for velocity and good to excellent between-unit reliability.

Sensor- and equation-based sit-to-stand power: The effect of age and functional limitations

Estimating lower-limb muscle power during sit-to-stand (STS) tests is feasible for large-scale implementation. This study investigated 1) whether age, functional limitations and sex have an influence on the movement strategy and power production during STS; and 2) potential differences between STS power estimated with either a simple equation or a sensor. Five-repetition STS data of 649 subjects (♂352 ♀297) aged 19 to 93 years were included. Subjects were divided in different age groups and levels of functioning. A body-fixed sensor measured (sub)durations, trunk movement (flexion/extension) and STS muscle power (P_sensor). Additionally, mean STS muscle power was calculated by a mathematic equation (Alcazar et al., 2018)Results revealed that 1) older subjects and women showed greater trunk flexion before standing up than younger subjects and men, respectively (both p < 0.001); 2) well-functioning adults seemed to have the tendency to not extend the trunk fully during the sit-to-stand transition (mean difference extension - flexion range = -15.3° to -13.1°, p < 0.001); 3) mobility-limited older adults spent more time in the static sitting and standing positions than their well-functioning counterparts (all p < 0.001); 4) STS power decreased with age and was lower in women and in limited-functioning subjects compared to men and well-functioning subjects, respectively (p < 0.05); 5) P_formula was highly related to P_sensor (ICC = 0.902, p < 0.001); and 6) P_formula demonstrated higher values than P_sensor in well-functioning adults [mean difference = -0.31 W/kg and -0.22 W/kg for men and women, respectively (p < 0.001)], but not among limited-functioning older adults. To conclude, this study showed that age and functional limitations have an influence on the movement strategy during a 5-repetition STS test. Differences in movement strategy can affect the comparison between P_formula and P_sensor. In well-functioning older adults, P_formula was slightly higher than P_sensor, which might be related to an incomplete extension in the sit-to-stand transition.

Cognitive and visual task effects on gaze behaviour and gait of younger and older adults

Cognitive dual tasks alter gait of younger and older adults and recent research has demonstrated that they also influence gaze behaviour and standing postural control. These findings suggest that age-related changes in cognitive and gaze function might increase fall risk in older adults. The purpose of this study was to determine the effect cognitive and visual dual tasks on the gait and gaze behaviour of younger and older adults. Ten older and ten younger adults walked for 3 min on a treadmill at preferred walking speed under three conditions, single task, cognitive and visual dual task conditions. Gait dynamics were measured using accelerometry and gaze behaviour was measured using wearable eye-trackers. Stride time variability and centre of mass (COM) motion complexity increased in dual-task conditions in older adults but had no difference for younger adults. Dual tasks had limited effect on gaze behaviour; however, visual input duration was greater, and visual input frequency and saccade frequency were lower in older than younger adults. The gaze adaptations in older adults may be the result of slower visual processing or represent a compensatory strategy to suppress postural movement. The increase in gait COM motion complexity in older adults suggests the dual tasks led to more automatic gait control resulting from both cognitive and visual tasks.

Modified Fukuda stepping motion assessment of young healthy adults using portable inertial measurement units

Currently, vestibular rehabilitation approaches used to monitor body position and movement during rehabilitation training require specialized equipment or rely on clinician observation. Thus, a simpler position-sensing approach that can be used to monitor movement during vestibular rehabilitation is required. This study used wearable motion sensors with built-in accelerometers and gyrometers to assess movement in adults. We explored stepping patterns in adults using this motion-sensing system. Six healthy adults (men, age 27.3 ± 5.8 years) underwent a modified Fukuda stepping test (Foulage test [FT]) while wearing a six-axis motion sensor (accelerometer: X-axis, Y-axis, Z-axis; gyrometer: X-axis, Y-axis, Z-axis) positioned at the head, thorax, and lumbar spine. For motion sensor parameters, we calculated the root mean square (RMS), autocorrelation coefficient (AC), power spectrum (PS) of the AC, and Euler angles from the six-axis motion sensor. For the FT parameters, the FT value, step variance, and θ values were calculated. Data were analyzed, and multivariable regression analysis was performed using the FT value, step variance, and θ value as the dependent variables to investigate their influence on dynamic equilibrium. The explanatory variables included the motion sensor parameters, RMS, AC, and PS. Our results suggested that almost no head and lumbar spine movement occurred while stepping with eyes open. Contrastingly, the head and lumbar spine swayed with eyes closed. This accelerometric and gyroscopic device is easy to use, does not require specialized equipment, and can be used to analyze performance in the modified Fukuda stepping test in clinical practice. Inertial sensors have many advantages over other sensing technologies.

Characterization of Musculoskeletal Injury Risk in Dungeness Crab Fishing

OBJECTIVES

Commercial Dungeness crab fishermen's manual crab pot handling activities can be done in harsh outdoor working environments at sea and can pose well-known physical risk factors associated with musculoskeletal injury including forceful exertion, repetition and awkward posture. The nonfatal injury rate in this fishing fleet is 3.4 per 1,000 full-time equivalent workers. Two-thirds of self-reported injuries in the fleet were musculoskeletal sprains and strains. To date, no objective biomechanical assessment of musculoskeletal disorder (MSD) risk has been conducted due to the challenging work environment.

METHODS

The aim of this study was to determine the feasibility of collecting objective biomechanical assessments (i.e., posture and repetition) using inertial measurement unit (IMU) sensors placed on the arms and torso of professional deckhands (n = 7) while at sea, harvesting Dungeness crab. Based on the IMU-measured posture data, fishermen's anthropometry, and crab pot weights, biomechanical loading of the low back and both shoulders was estimated.

RESULTS

The IMU sensor data showed that commercial Dungeness crab fishing is highly repetitive and poses awkward postures in the shoulders and back. The estimated static low back compression, shear force, and flexion moment about the shoulders and low back (L5/S1) indicate potential injury risk associated with harvesting crab.

CONCLUSION

The results indicate that objective biomechanical assessment using the IMU sensors is feasible in the commercial fishing environment.

Angle measurement stability and cycle counting accuracy of hours-long duration IMU based arm motion tracking with application to normal shoulder ADLs

BACKGROUND

Inertial measurement units are increasing used for monitoring joint motion, but there is a need to demonstrate their suitability during hours-long continuous use, as well as a need for validated methods to count arm cycles and provide descriptions of typical cycles.

RESEARCH QUESTION

Do IMU sensors and rainflow counting have sufficient accuracy for tracking and cycle counting of hours-long continuous arm motion? If so, what are the cycle rates of normal arm ADL and is there a representative cycle that can serve as a 'gait cycle' for the arm?

METHODS

IMU sensors continuously tracked a robot performing 8 h of simulated cyclic arm motion. Error in the angle measurements was regressed against time to determine the rate of error and the total accumulated error. Additionally, the cycle count accuracy of rainflow, peak/valley, and Fourier transform counting methods was evaluated.

RESULTS

Over 8 h the IMU measurements accumulated a maximum 0.473° of error and the rainflow method counted cycles with less than 1% error. Applying rainflow counting to normal shoulder ADL resulted in an average rate of 533 elevation cycles per day.Tabulating the ADL cycles by mean and range values into a matrix and calculating the centroid, the single best values representing arm elevation cycles were a mean of 22.4° and a range of 21.6°.

SIGNIFICANCE

IMU sensors can track arm motion for 8 h with little increase in error, though during longer durations error may reach unacceptable levels. For normal arm ADL, the rainflow determined count of arm elevation full-cycles differed from previous estimates based on peak/valley counting. From the rainflow counting, a single cycle representation of cycle mean and range was determined that can be used as a 'gait cycle' for the shoulder.

Open-source software library for real-time inertial measurement unit data-based inverse kinematics using OpenSim

Background

Inertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim's functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics.

Methods

We developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub.

Results

A standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics.

Conclusions

The software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory.

Stride frequency derived from GPS speed fluctuations in galloping horses

Changes in gallop stride parameters prior to injury have been documented previously in Thoroughbred racehorses. Validating solutions for quantification of fundamental stride parameters is important for large scale studies investigating injury related factors. This study describes a fast Fourier transformation-based method for extracting stride frequency (SF) values from speed fluctuations recorded with a standalone GPS-logger suitable for galloping horses. Limits of agreement with SF values derived from inertial measurement unit (IMU) pitch data are presented. Twelve Thoroughbred horses were instrumented with a GPS-logger (Vbox sport, Racelogic, 10 Hz samplerate) and a IMU-logger (Xsens DOT, Xsens, 120 Hz samplerate), both attached to the saddlecloth in the midline caudal to the saddle and time synchronized by minimizing root mean square error between differentiated GPS and IMU heading. Each horse performed three gallop trials with a target speed of 36miles per hour (16.1 ms^-1) on a dirt racetrack. Average speed was 16.48 ms^-1 ranging from 16.1 to 17.4 ms^-1 between horses. Limits of agreement between GPS- and IMU-derived SF had a bias of 0.0032 Hz and a sample-by-sample precision of +/-0.027 Hz calculated over N = 2196 values. The stride length uncertainty related to the trial-by-trial SF precision of 0.0091 Hz achieved across 100 m gallop sections is smaller than the 10 cm decrease in stride length that has been associated with an increased risk of musculoskeletal injury. This suggests that the described method is suitable for calculating fundamental stride parameters in the context of injury prevention in galloping horses.

Mind the gap - development of conversion models between accelerometer- and IMU-based measurements of arm and trunk postures and movements in warehouse work

Sensor type (accelerometers only versus inertial measurement units, IMUs) and angular velocity computational method (inclination versus generalized velocity) have been shown to affect the measurements of arm and trunk movements. This study developed models for conversions between accelerometer and IMU measurements of arm and trunk inclination and between accelerometer and IMU measurements of inclination and generalized (arm) velocities. Full-workday recordings from accelerometers and IMUs of arm and trunk postures and movements from 38 warehouse workers were used to develop 4 angular (posture) and 24 angular velocity (movement) conversion models for the distributions of the data. A power function with one coefficient and one exponent was used, and it correlated well (r2 > 0.999) in all cases to the average curves comparing one measurement with another. These conversion models facilitate the comparison and merging of measurements of arm and trunk movements collected using the two sensor types and the two computational methods.

An Alternative Athlete Monitoring System Using Cost-Effective Inertial Sensing Instrumentation

An examination of the human gait is feasible using inertial sensing. The embedded accelerometer and gyroscope in an inertial measurement unit can evaluate physical activity-based sports and this unit is relatively affordable compared to global positioning systems or video recording quantification. This study developed a cost-effective sports monitoring investigation method with an inertial sensor attached to the right leg of the athletes. In total, four parameters were simultaneously tracked to assess the entire sensor performance in real-time. The accelerometer measured the typical leg angle when walking and running, whereas the gyroscope processed the raw data to obtain the stride frequency from the time-domain data. Moreover, a comparison between the accelerometer and gyroscope was presented while simultaneously attaining the signal to convert the time-domain data to frequency results. Also, the number of strides and linear velocity was expressed as results in this study. To confirm the results, a statistical hypothesis test was implemented for all obtained results. The results indicated that the inertial sensing instrumentation used in this study is promising and could be an affordable alternative option for a sports monitoring system.

Temporal and kinematic analyses of timed up and go test in chronic low back pain patients

PURPOSE

To analyze temporal and kinematic parameters of chronic Low-Back Pain (cLBP) subjects compared to healthy subjects during Timed Up and Go Test (TUG) execution implemented with an Inertial Measurement Unit and to explore the correlations of those parameters with pain and disability.

METHODS

Observational cross-sectional study. Thirty-one subjects with cLBP [(19 females - 61%), mean age 61 ± 19] were allocated to the case group, and 14 healthy [(10 females - 71%), mean age 62 ± 6] subjects to the control group. Instrumented TUG was administered to both groups. The Roland Morris Disability Questionnaire and Numerical Pain Rating Scale (NPRS) were also administered for disability and pain assessment in the case group.

RESULTS

Mean TUG time to completion [12.2 ± 3.5 s for cLBP; 8.1 ± 0.9 s for healthy] and the most of sub-phases duration significantly differed between groups (p < 0.05). As for kinematic parameters, significant differences (p < 0.05) were mainly retrieved in acceleration components during the sit-to-stand and stand-to-sit phase, with the cLBP group showing lower accelerations. Significant correlation [from strong (ρ = 0.75 of time to completion) to moderate (ρ = 0.43 of sit-to-stand)] was observed between RMQD score and all temporal parameters and with most of the kinematic parameters. No correlation with NPRS score was found.

CONCLUSIONS

Instrumented TUG application into a cLBP population provides valuable information about movement behaviors with a deeper assessment of objective functional impairment and disability in respect of the classical stop-watch outcome of TUG, possibly allowing a better design of the rehabilitative intervention.

Free-living gait does not differentiate chronic mTBI patients compared to healthy controls

Background

Physical function remains a crucial component of mild traumatic brain injury (mTBI) assessment and recovery. Traditional approaches to assess mTBI lack sensitivity to detect subtle deficits post-injury, which can impact a patient’s quality of life, daily function and can lead to chronic issues. Inertial measurement units (IMU) provide an opportunity for objective assessment of physical function and can be used in any environment. A single waist worn IMU has the potential to provide broad/macro quantity characteristics to estimate gait mobility, as well as more high-resolution micro spatial or temporal gait characteristics (herein, we refer to these as measures of quality). Our recent work showed that quantity measures of mobility were less sensitive than measures of turning quality when comparing the free-living physical function of chronic mTBI patients and healthy controls. However, no studies have examined whether measures of gait quality in free-living conditions can differentiate chronic mTBI patients and healthy controls. This study aimed to determine whether measures of free-living gait quality can differentiate chronic mTBI patients from controls.

Methods

Thirty-two patients with chronic self-reported balance symptoms after mTBI (age: 40.88 ± 11.78 years, median days post-injury: 440.68 days) and 23 healthy controls (age: 48.56 ± 22.56 years) were assessed for ~ 7 days using a single IMU at the waist on a belt. Free-living gait quality metrics were evaluated for chronic mTBI patients and controls using multi-variate analysis. Receiver operating characteristics (ROC) and Area Under the Curve (AUC) analysis were used to determine outcome sensitivity to chronic mTBI.

Results

Free-living gait quality metrics were not different between chronic mTBI patients and controls (all p > 0.05) whilst controlling for age and sex. ROC and AUC analysis showed stride length (0.63) was the most sensitive measure for differentiating chronic mTBI patients from controls.

Conclusions

Our results show that gait quality metrics determined through a free-living assessment were not significantly different between chronic mTBI patients and controls. These results suggest that measures of free-living gait quality were not impaired in our chronic mTBI patients, and/or, that the metrics chosen were not sensitive enough to detect subtle impairments in our sample.

Generalizability of deep learning models for predicting outdoor irregular walking surfaces

Observations from laboratory-based gait analysis are difficult to extrapolate to real-world environments where gait behavior is modulated in response to complex environmental conditions and surface profiles. Inertial measurement units (IMUs) permit real-world gait analysis; however, automatic detection of surfaces encountered remains largely unexplored. The aims of this study are to quantify for machine learning models the effect of (1) random and subject-wise data splitting and (2) sensor location and count on surface classification performance. Thirty participants walked on nine surface conditions (flat-even, slope-up, slope-down, stairs-up, stairs-down, cobblestone, grass, banked-left, banked-right) wearing IMUs (wrist, trunk, bilateral thighs, bilateral shanks). Data were separated into gait cycles, normalized to 101 samples, and spilt into train and test sets (85 and 15%, respectively). For random splitting, trials were randomly assigned to the train or test set. In subject-wise splitting, all trials from 4 random participants were selected for testing. Linear discriminant analysis extracted features from the IMUs. Features were delivered to a neural network. F1-score evaluated model performance. Models achieved F1 scores of 0.96 and 0.78 using random and subject-wise splitting, respectively. Random splitting performance was mainly invariant to sensor location/count; however, subject-wise splitting showed best performance using lower-limb sensors. In general, stairs and sloped surfaces were easily predicted (F1 > 0.85) while banked surfaces were challenging, especially for subject-wise models (F1 ≈ 0.6). Neural networks can detect surfaces based on subtle changes in walking behavior captured by IMUs. Data splitting approaches and sensor location/count (subject-wise) have a non-negligible effect on model performance.

Assessment of countermovement jump with and without arm swing using a single inertial measurement unit

The countermovement vertical jump height, flight time, and jump duration are used to assess athletic performance. Force-plate and motion-capture cameras are used to estimate these parameters, yet, their application is limited to dedicated lab environments. Despite the potential of inertial measurement units (IMU) for estimating the jump height, their accuracy has not been validated. This study investigates the accuracy of our proposed method to estimate the jump height using a sacrum-mounted IMU, during countermovement jumping. Eleven individuals performed four jumps each. To obtain the jump height, we transformed the IMU readouts into anatomical planes, and double-integrated the vertical acceleration after correction for zero velocity and vertical displacement. The accuracy of jump height obtained by IMU was compared to force-plate and motion-capture cameras during jumps without arm swing (mean error (standard deviation) of 0.3(2.2) cm and 1.0(3.0) cm, and correlation coefficient of 0.83 and 0.82, respectively) and during jumps with arm swing (-1.1(2.1) cm and 0.5(1.9) cm, and 0.92 and 0.89). The correlation coefficients were high, and the errors were comparable to the difference between the jump height obtained by force-plate and cameras. Therefore, a sacrum-mounted IMU can be recommended for in-field assessment of countermovement jump with and without arm swing.

Measurement of uni-planar and sport specific trunk motion using magneto-inertial measurement units: The concurrent validity of Noraxon and Xsens systems relative to a retro-reflective system

BACKGROUND

There is a range of magneto-inertial measurement unit (MIMU) systems commercially available, however sensor specifications and fusion methods vary considerably between manufacturers. Such variability can influence the concurrent validity of MIMUs relative to reference standard measurement devices. Different MIMUs have been compared during static or low-velocity conditions, with higher-velocity movements assessed in robotic-based studies. However, there is a need for the concurrent validity of higher-velocity movements to be established in human-based studies.

RESEARCH QUESTION

This study aimed to assess the concurrent validity of two commercial MIMU systems (Noraxon and Xsens), relative to a 'gold-standard' retro-reflective motion capture system, when measuring trunk angles during uni-planar range of motion (ROM) and cricket bowling, which involves high-speed, multi-planar movements.

METHODS

For this criterion-based validity study, both MIMU systems incorporated comparable sensor specifications and employed Kalman filter sensor fusion algorithms. The MIMU based angles were compared with angles derived from concurrently captured three-dimensional retro-reflective data for 10 fast-medium bowlers. Statistical parametric mapping and root mean squared differences (RMSD) were computed for both MIMU systems.

RESULTS

One-dimensional statistical parametric mapping showed no significant differences for angles from both MIMU systems when compared with retro-reflective based angle outputs. The MIMU systems produced ROM RMSDs between 1.4 ± 1.0° and 2.6 ± 1.5°. One system displayed RMSDs between 4.6 ± 1.4° and 7.4 ± 1.9° during bowling, indicating functionally relevant differences to retro-reflective derived angles. There were some small but statistically significant differences in RMSDs between the MIMU systems.

SIGNIFICANCE

MIMU-based angle accuracy is poorer during high-speed, multi-planar movement than uni-planar tasks. Comparable MIMU systems can produce varying measurements during ROM and bowling tasks. It is likely that varying sample rates and sensor fusion algorithm parameters contributed to the differences.

Assessment of postural sway with a pendant-mounted wearable sensor

BACKGROUND

Body-worn inertial measurement unit (IMU) sensors have been widely used in postural stability and balance studies because of their low cost and convenience. In most of these studies, a single IMU sensor is attached to a waist belt near the body's center of mass. Some populations such as pregnant women, however, may find a waist belt challenging in terms of fit and comfort. For this reason it may be useful to identify an alternative location for placement of an IMU and a more comfortable means for attaching the sensor to the body. Research question Does placing an IMU sensor in a pendant worn around the neck permit discrimination between conditions with varying postural stability?

METHODS

Twenty-six healthy participants performed three standing tasks (double-leg, tandem, and single-leg standing) under eyes-open and eyes-closed vision conditions to preliminarily assess the ability of the pendant sensor to discriminate between balance conditions. Discrimination based upon data from a belt-mounted IMU was assessed in the same trials. Differences in standard deviation of acceleration components, sway area, and jerkiness due to trial condition and sensor were evaluated using analysis of variance followed by post hoc comparisons. These data were also incorporated into receiver-operator characteristic (ROC) curve analysis to assess the effectiveness of each sensor at discriminating between conditions.

RESULTS

Stability was found to vary across conditions, but there was no interaction between stability and sensor location (all p ≥ 0.323). ROC curve analysis showed that sensors in both locations were good discriminators between conditions. Significance Placing an IMU in a pendant may be feasible for studying and monitoring postural instability. This approach may be especially valuable when considering populations for which wearing a belt is uncomfortable.

Real-time motion onset recognition for robot-assisted gait rehabilitation

BACKGROUND

Many patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users' movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total).

METHODS

To predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification.

RESULTS

Testing five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of [Formula: see text] and [Formula: see text] are achieved on testing using LOPOCV and test data ([Formula: see text], participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = [Formula: see text]).

CONCLUSIONS

A neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

Obtaining wheelchair kinematics with one sensor only? The trade-off between number of inertial sensors and accuracy for measuring wheelchair mobility performance in sports

In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1°/s for wheelchair angular velocity when compared with the reference system. The two-IMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0°/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available.

Functional calibration does not improve the concurrent validity of magneto-inertial wearable sensor-based thorax and lumbar angle measurements when compared with retro-reflective motion capture

 Magneto-inertial measurement unit (MIMU) systems allow calculation of simple sensor-to-sensor Euler angles, though this process does not address sensor-to-segment alignment, which is important for deriving meaningful MIMU-based kinematics. Functional sensor-to-segment calibrations have improved concurrent validity for elbow and knee angle measurements but have not yet been comprehensively investigated for trunk or sport-specific movements. This study aimed to determine the influence of MIMU functional calibration on thorax and lumbar joint angles during uni-planar and multi-planar, sport-specific tasks. It was hypothesised that functionally calibrating segment axes prior to angle decomposition would produce smaller differences than a non-functional method when both approaches were compared with concurrently collected 3D retro-reflective derived angles. Movements of 10 fast-medium cricket bowlers were simultaneously recorded by MIMUs and retro-reflective motion capture. Joint angles derived from four different segment definitions were compared, with three incorporating functionally defined axes. Statistical parametric mapping and root mean squared differences (RMSD) quantified measurement differences one-dimensionally and zero-dimensionally, respectively. Statistical parametric mapping found no significant differences between MIMU and retro-reflective data for any method across bowling and uni-planar trunk movements. The RMSDs for the functionally calibrated methods and non-functional method were not significantly different. Functional segment calibration may be unnecessary for MIMU-based measurement of thorax and lumbar joint angles.

Quantifying throwing load in handball: a method for measuring the number of throws

Shoulder injuries are a common problem in handball. One likely cause of such injuries is excessive throwing. However, it is difficult to measure the number of player throws in large cohort studies using existing methods accurately. Therefore, the purpose of this study is to develop and validate a method for identifying overhead throws using a low-cost inertial measurement unit (IMU) worn on the wrist. In a two-stage approach, we developed a threshold-based automatic identification method for overhead throws in a laboratory study using the IMU. Subsequently, we validated the suggested thresholds in a field setting by comparing throws identified by the threshold-method to throws identified by video recordings of handball practices. The best set of threshold values resulted in a per-player median sensitivity of 100% (range: 84-100%) and a median positive predictive value (PPV) of 96% (range: 86-100%) in the development study. In the validation study, the per-player median sensitivity dropped to 78% sensitivity (range: 52-91%), while the per-player median PPV dropped to 79% (range: 47-90%). The proposed method is a promising method for automatically identifying handball throws in a cheap and feasible way.

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention.

The importance of inertial measurement unit placement in assessing upper limb motion

Motion analysis using inertial measurement units (IMU) has emerged as an alternative to optical motion capture. However, the validity and reliability of upper limb measurements varies significantly between studies. The objective of this study was to determine how sensor placement affects kinematic output in the assessment of motion of the arm, shoulder, and scapula. IMUs were placed proximally/distally on arms, and medially/laterally on the scapula, in a group of eleven healthy participants, while performing nine different motion tasks. Linear regressions and mixed models analysed how these different sensor placements affected the estimated joint motion by establishing the linear relationship between sensors placed on the same body segment. The placement of sensors affected the measured kinematic output considerably, most prominent affect was seen for sensor placement on scapula during flexion and abduction, and on forearm during pronation/supination. The slope of the linear regression lines was 2.5 during flexion, 2.7 during abduction, and 1.8 for forearm pronation/supination. The results of this study suggest that the forearm sensor should be placed on the dorsal side of the forearm, at the distal end; the upper arm sensor should be placed laterally, on the distal part of the arm; and the sensor on the scapula should be placed cranially, along the spine of scapula.

Functional limits of stability and standing balance in people with Parkinson's disease with and without freezing of gait using wearable sensors

BACKGROUND

People with from Parkinson's disease (PD) and freezing of gait (FoG) have more frequent falls compared to those who do not freeze but there is no consensus on which, specific objective measures of postural instability are worse in freezers (PD + FoG) than non-freezers (PD-FoG).

RESEARCH QUESTION

Are functional limits of stability (fLoS) or postural sway during stance measured with wearable inertial sensors different between PD + FoG versus PD-FoG, as well as between PD versus healthy control subjects (HC)?

METHODS

Sixty-four PD subjects with FoG (MDS-UPDRS Part III: 45.9 ± 12.5) and 80 PD subjects without FoG (MDS-UPDRS Part III: 36.2 ± 10.9) were tested Off medication and compared with 79 HC. Balance was quantified with inertial sensors worn on the lumbar spine while performing the following balance tasks: 1) fLoS as defined by the maximum displacement in the forward and backward directions and 2) postural sway area while standing with eyes open on a firm and foam surface. An ANOVA, controlling for disease duration, compared postural control between groups.

RESULTS

PD + FoG had significantly smaller fLoS compared to PD-FoG (p =  0.004) and to healthy controls (p <  0.001). However, PD-FoG showed similar fLoS compared to healthy controls (p =  0.48). Both PD+FoG and PD-FoG showed larger postural sway on a foam surface compared to healthy controls (p =  0.001) but there was no significant difference in postural sway between PD+FoG and PD-FoG.

SIGNIFICANCE

People with PD and FoG showed task-specific, postural impairments with smaller fLoS compared to non-freezers, even when controlling for disease duration. However, individuals with PD with or without FoG had similar difficulties standing quietly on an unreliable surface compared to healthy controls. Wearable inertial sensors can reveal worse fLoS in freezers than non-freezers that may contribute to FoG and help explain their more frequent falls.