Inertial Sensors Research Articles

Adaptive Movement Analysis And Enhanced Self-Monitoring Hypercube For Parkinson’s Disease

Parkinson’s Disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments, including tremors, rigidity, bradykinesia, and postural instability, which progressively diminish patients’ ability to perform daily activities independently. Current rehabilitation practices, primarily focused on physical therapy, medication, and traditional support devices, provide essential symptom management but often lack customization to individual patient needs, leading to inconsistent adherence and limited long-term benefits. This paper introduces the Adaptive Movement Analysis and Enhanced Self-Monitoring Hypercube, a novel rehabilitation framework aimed at improving PD management through a comprehensive, adaptive approach.The system leverages cutting-edge sensors, including the MPU6050 Inertial Measurement Unit (IMU) and infrared proximity sensors, integrated with a Teensy microcontroller to deliver real-time movement analysis and tailored exercise feedback. Through Bluetooth-enabled connectivity, patient data is transmitted to a digital platform where it can be analyzed and adjusted based on performance metrics. Additionally, gamification techniques, task-specific training modules, and customizable feedback mechanisms are embedded to increase patient engagement and adherence to exercise routines. This adaptive solution provides a structured, motivating environment that addresses the fluctuating capabilities of PD patients, promoting functional recovery and enhanced self-monitoring.

Quantifying Hand Motion Complexity in Simulated Sailing Using Inertial Sensors.

The control of hand movement during sailing is important for performance. To quantify the amount of regularity and the unpredictability of hand fluctuations during the task, the mathematical algorithm Approximate Entropy (ApEn) of the hand acceleration can be used. Approximate Entropy is a mathematical algorithm that depends on the combination of two input parameters including (1) the length of the sequences to be compared (m), and (2) the tolerance threshold for accepting similar patterns between two segments (r). The aim of this study is to identify the proper combinations of 'm' and 'r' parameter values for ApEn measurement in the hand movement acceleration data during sailing. Inertial Measurement Units (IMUs) recorded acceleration data for both the mainsail (non-dominant) and tiller (dominant) hands across the X-, Y-, and Z-axes, as well as vector magnitude. ApEn values were computed for 24 parameter combinations, with 'm' ranging from 2 to 5 and 'r' from 0.10 to 0.50. The analysis revealed significant differences in acceleration ApEn regularity between the two hands, particularly along the Z-axis, where the mainsail hand exhibited higher entropy values (p = 0.000673), indicating greater acceleration complexity and unpredictability. In contrast, the tiller hand displayed more stable and predictable acceleration patterns, with lower ApEn values. ANOVA results confirmed that parameter 'm' had a significant effect on acceleration complexity for both hands, highlighting differing motor control demands between the mainsail and tiller hands. These findings demonstrate the utility of IMU sensors and ApEn in detecting nuanced variations in acceleration dynamics during sailing tasks. This research contributes to the understanding of hand-specific acceleration patterns in sailing and provides a foundation for further studies on adaptive sailing techniques and motor control strategies for both novice and expert sailors.

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition

Human activity recognition is a critical task for various applications across healthcare, sports, security, gaming, and other fields. This paper presents BodyFlow, a comprehensive library that seamlessly integrates human pose estimation and multiple-person estimation and tracking, along with activity recognition modules. BodyFlow enables users to effortlessly identify common activities and 2D/3D body joints from input sources such as videos, image sets, or webcams. Additionally, the library can simultaneously process inertial sensor data, offering users the flexibility to choose their preferred input, thus facilitating multimodal human activity recognition. BodyFlow incorporates state-of-the-art algorithms for 2D and 3D pose estimation and three distinct models for human activity recognition.

On-field concurrent validity of flight time-based countermovement jump height during fatigue: A comparison between inertial measurement units worn at different locations on the body

ABSTRACT We explored the feasibility of utilizing immediate changes in flight time-based vertical countermovement jump height as an on-field measure for fatigue-induced decrements in performance. Comparing Inertial Measurement Units (IMUs) worn at five body locations (feet, shorts for thighs and pelvis, waist strap, and thorax in a standard GPS vest) with a force plate as a reference, we enlisted 19 amateur football players who performed a series of 10 maximal 30 m sprints (initiated every minute). Maximal jumps were executed immediately before and after each sprint, with the latter jumps recorded on a force plate integrated into the field. Bland Altman’s bias (−0.49 cm) and limits of agreement (1.01 cm) were minimal for the feet IMUs. The thorax IMU had the highest bias (−6.35 cm), but the limits of agreement (2.73 cm) were similar to the other locations. Repeated measures correlations (rmcorr) between force plate and IMUs were excellent for the feet (rmcorr = 0.98) and good for the thorax (rmcorr = 0.86) and other locations except for the waist strap. In the fatigued state, within-session coefficients of variation ranged from 4.0% (pelvis in shorts) to 6.5% (waist strap). These findings suggest that body-worn IMUs possess the potential for a prompt and straightforward on-field vertical jump assessment to monitor acute fatigue.

GNSS-assisted optimal alignment method for low-cost SINS motion of vehicle

Abstract To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.

Acceptability, validity and responsiveness of inertial measurement units for assessing motor recovery after gene therapy in infants with early onset spinal muscular atrophy: a prospective cohort study

BackgroundOnasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT.MethodsClinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors.Results23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001).ConclusionIMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds.

The shift of the horse breeding sector from agricultural to leisure and sports purposes led to a decrease in local breeds' population size due to the loss of their original breeding purposes. Most of the Italian breeds must adapt to modern market demands, and gait traits are suitable phenotypes to help this process. Inertial measurement unit (IMU) technology can be used to objectively assess them. This work aims to investigate on IMU recorded data (i) the influence of environmental factors and biometric measurements, (ii) their repeatability, (iii) the correlation with judge evaluations, and (iv) their predictive value. The Equisense Motion S® was used to collect phenotypes on 135 horses, Bardigiano (101) and Murgese (34) and the data analysis was conducted using R (v.4.1.2). Analysis of variance (ANOVA) was employed to assess the effects of biometric measurements and environmental and animal factors on the traits. Variations in several traits depending on the breed were identified, highlighting different abilities among Bardigiano and Murgese horses. Repeatability of horse performance was assessed on a subset of horses, with regularity and elevation at walk being the traits with the highest repeatability (0.63 and 0.72). The positive correlation between judge evaluations and sensor data indicates judges' ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). A high variability was observed in the accuracy of the SVM model, ranging from 55 to 100% while the other two models showed higher consistency, with accuracy ranging from 74 to 100% for the GBM and from 64 to 88% for the KNN. Overall, the GBM model exhibits the highest accuracy and the lowest error. In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training.

Uncertainty-Aware Depth Network for Visual Inertial Odometry of Mobile Robots

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin.

An innovative virtual sensing system for the vehicle-centric evaluation of emissions in the sustainable mobility transition

The objective of the research is to develop a new methodology capable of quantifying the emissions of internal combustion engine cars based not only on their technology but also on the dynamic behavior of the individual vehicle. No complicated exhaust measurement apparatus is needed. Employing just a telematic device equipped with GNSS and an inertial measurement unit, which can be easily attached to the vehicle, we demonstrate how individualised, accurate climate-altering emissions result by using figures extracted from European test databases, appropriately combined with real travelled distances, speeds, and driving style of each individual car. Pollutant emissions are, in this paper, also assessed though in a more general manner, and this represents a starting point for a future complete vehicle-centric emissions evaluation. This research demonstrates that circulating car emissions have to be assessed considering not only the Euro class and type of fuel, but also how the vehicle is driven. In this work a new, implementation oriented methodology is proposed in order to properly merge average European standard emission data with car-based telematic measures to highlight a new paradigm for regulating in a more conscious way the transition of the internal combustion engine car park towards a large scale, sustainable mobility ecosystem. This approach would place greater responsibility on the driver, without leaving anyone behind and without compelling the car scrapping solely based on the Euro class of its engine. The key contribution is ultimately to establish an easy to handle methodology enabling targeted and behavior-based traffic management policies. In fact, through a massive telematics recent dataset, the proposed virtual emission sensing system reveals that an extensive and inefficient use of eco-friendly vehicles produces much more greenhouse gas emissions than older vehicles driven in an environmentally responsible way.

Methods for Evaluating Tibial Accelerations and Spatiotemporal Gait Parameters during Unsupervised Outdoor Movement.

The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5-20 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running.

A New Scene Sensing Model Based on Multi-Source Data from Smartphones

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm.

Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.Design/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.FindingsThe findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.Practical implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.Social implicationsDuring our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.Originality/valueThe utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.

Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning Approaches.

Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learning (ML) techniques, and two deep learning (DL) models are applied in the proposed identification system. For the experiments, 24 FS patients and 20 healthy subjects were recruited to perform five daily shoulder tasks with two IMUs attached to the arm and the wrist. The results demonstrate that the proposed system using deep learning presented the best identification performance using all features. The convolutional neural network achieved the best identification accuracy of 88.26%, and the multilayer perceptron obtained the best F1 score of 89.23%. Further analysis revealed that the identification performance based on wrist features had a higher accuracy compared to that based on arm features. The system's performance using time-domain statistical features has better discriminability in terms of identifying FS compared to using kinematic features. We demonstrate that the implementation of the IMU-based identification system using ML is feasible for FS assessment in clinical practice.

A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.

Inertial measurement units to evaluate the efficacity of Equino Varus Foot surgery in post stroke hemiparetic patients: a feasibility study

IntroductionThis study evaluates the gait analysis obtained by Inetial Measurement Units (IMU) before and after surgical management of Spastic Equino Varus Foot (SEVF) in hemiplegic post-stroke patients and to compare it with the functional results obtained in a monocentric prospective cohort.MethodsPatients with post-stroke SEVF, who underwent surgery in a single hospital between November 2019 and December 2021 were included. The follow-up duration was 6 months and included a functional analysis using Goal Attainment Scaling (GAS) and a Gait analysis using an innovative Multidimensional Gait Evaluation using IMU: the semiogram.Results20 patients had a gait analysis preoperatively and at 6 months postoperatively. 90% (18/20) patients had a functional improvement (GAS T score ≥ 50) and 50% (10/20) had an improvement in walking technique as evidenced by the cessation of the use of a walking aid (WA). In patients with functional improvement and modification of WA the change in the semiogram area was + 9.5%, sd = 27.5%, and it was + 15.4%, sd = 28%. In the group with functional improvement without change of WA. For the 3 experiences (two patients) with unfavorable results, the area under the curve changed by + 2.3%, -10.2% and − 9.5%. The measurement of the semiogram area weighted by average speed demonstrated very good reproducibility (ICC(1, 3) = 0.80).DiscussionIMUs appear to be a promising solution for the assessment of post-stroke hemiplegic patients who have undergone SEVF surgery. They can provide a quantified, objective, reliable in individual longitudinal follow up automated gait analysis solution for routine clinical use. Combined with a functional scale such as the GAS, they can provide a global analysis of the effect of surgery.

Modified tai chi movement training based on sEMG and movement analysis on improving upper extremities motor function: a protocol for a clinical randomised controlled trial

IntroductionStroke survivors often face motor dysfunction, increasing fall risk. Lower extremity muscle weakness is a key factor affecting walking ability. Tai chi (TC) has been shown to improve muscle strength...

Boostering motor imagery processing to improve gait in patients with Parkinson disease and freezing of gait: A pilot study

BackgroundGiven that patients with Parkinson's Disease (PD) and Freezing of Gait (FoG) may lack the cognitive resources necessary to activate the motor imagery (MI) process, investigating how to boost MI vividness and accuracy could be a valuable therapeutic strategy in MI Practice (MIP). ObjectiveWe aim to evaluate the priming effect of visual, or auditory, or attentional stimuli in enhancing MI ability by using quantitative data on gait and turning performance. MethodsNineteen PD participants with FoG underwent four one-week sessions of MIP, with pre and post clinical assessments. Each session included MI alone or one of three booster MI tasks (Attentional, Action observation, or Auditory) before imagining and executing walking straight and performing a 180° turn. Gait and turning performances were evaluated using six inertial sensors before and after each session. ResultsOur findings showed that both MI and boosted MI induced similar improvement in gait (speed and stride length) and 180° (step number and velocity) and 360° turning (velocity, angle) parameters compared to baseline. When differences among “booster” tasks were analyzed, results showed that Auditory and Attentional boosted MI were superior to MI alone in some gait and turning parameters. At the end of the 4 sessions, MI ability measured by means of Kinesthetic and Visual Imagery Questionnaire and Gait Imagery Questionnaire and FoG symptoms were also improved. ConclusionOur preliminary results suggest that boosting MI is a feasible strategy for enhancing MI ability and addressing FoG symptoms. Auditory and Attentional conditions appear to enhance the priming effect of MI on gait and turning performance more effectively.

Complex Exercises Improve Cognition in People With Parkinson's Disease and Freezing of Gait.

Evidence has suggested that cognitive decline may be a risk factor for freezing of gait (FOG) in Parkinson's disease (PD). Complex and challenging exercises have been suggested as potential rehabilitation strategies to decrease FOG severity and improve cognition; however, it is unknown whether improvement in cognition would explain decreased FOG severity following exercise. In this secondary analysis, we evaluated the effects of the adapted resistance training with instability (ARTI-complex and challenging exercises) compared with traditional motor rehabilitation (TMR-without challenging exercises) on cognitive function in people with FOG of PD. We also verified whether cognitive improvement explains the decrease in FOG previously published. Participants were randomized to either the experimental group (ARTI, n = 17) or the active control group (TMR, n = 15). Both training groups exercised 3 times a week for 12 weeks (80-90 minute each session). FOG severity (FOG ratio from inertial sensors during a 360° turning-in-place task), frontal lobe function (Frontal Assessment Battery [FAB]), global cognition (Montreal Cognitive Assessment [MoCA]), and attention and psychomotor speed (Digit Symbol Substitution Test [DSST]) were evaluated before and after interventions. Only the ARTI group improved FAB, MoCA, and DSST scores at posttraining. In addition, ARTI was more effective than TMR in improving FAB scores at posttraining. The changes in FAB scores explained the changes in FOG ratio following ARTI (R2 = .43, P < .01). This pilot study suggests that ARTI, a complex and challenging training, improves cognition in people with FOG of PD. Improvements in frontal lobe function with ARTI help explain decreased FOG severity.

SM-VINS: A Fast and Decoupled Monocular Visual–Inertial Sensors SLAM System With Stepwise Marginalization

SM-VINS: A Fast and Decoupled Monocular Visual–Inertial Sensors SLAM System With Stepwise Marginalization