Freezing of gait (FoG) is a widely observed movement disorder in Parkinson’s Disease patients (PD). Its prediction is crucial for effectively giving the cue to avoid FoG occurrence. However, present methods of prediction of FoG are inaccurate for large but practical prediction horizons (PH)s. Therefore, this work presents a comprehensive analysis of the electroencephalography (EEG) and inertial measurement units (IMU)s to predict FoG advance in time. An ensemble model consisting of two neural networks, EEGFoGNet and IMUFoGNet, was developed and tested at different PHs and ensemble weights. Moreover, the model is tested for two practical scenarios: clinical or research applications and personal uses. For clinical or research applications, stratified 5-Folds cross-validation was used. For personal uses, a transfer learning technique was used for learning user-specific FoG-related features. The model obtained the best accuracy of 92.1% at 1 second’s PH and the least accuracy of 86.2% at 5 seconds’ PH. The presented results are encouraging and show the proposed model’s clinical applicability. This study will also help practitioners in comparing the efficacy of different cueing methods.

The unavailability of a reliable and affordable gait assessment system for quantifying gait abnormality is responsible for the slow and inaccurate diagnosis of gait disorders. This study explores the feasibility of using a low-cost lower limb kinematics estimation device for clinical applications. The low-cost lower limb kinematics estimation device used in the study consists of an instrumented outsole (IO), an instrumented insole (II), and an artificial intelligence model (foot2hip). The low-cost system was compared with the standard system for reconstruction errors. Four analyses were performed to understand the nature of performance variability concerning clinically relevant parameters, including automated-gait assessment score (A-GAS). The study’s results suggest that the proposed system is unsuitable for detailed gait analysis (in the swing phase). However, the device can be used for personal use for tracking global gait abnormalities, and for clinical use in the screening of patients. The results of the analyses will help the users of the proposed system in framing the optimum data collection protocol.

Objective: This study aims to develop a neural network (foot2hip) for long-term recording of gait kinematics with improved user comfort. Methods: Foot2hip predicts ankle, knee, and hip joint angle profiles in the sagittal plane using foot kinematics and kinetics during walking. Foot2hip consists of three convolution, two max-pooling, two LSTM and three dense layers. An indigenously developed insole and an outsole were used to measure the kinetics and kinematics of the foot, respectively. Seven healthy participants were recruited to follow an experimental protocol consisting of six walking conditions: slow, medium, fast walking speed, rearfoot, flatfoot, and forefoot landing pattern. Results: When tested for leave-one-out and nested cross-validation, foot2hip obtained 3.04° ±0.20 RMSE and 0.97±0.01 correlation coefficient for knee joint, 1.7°± 0.09 RMSE and 0.95±0.01 correlation coefficient for hip joint, and 1.32°±0.08 RMSE and 0.91±0.02 correlation coefficient for ankle joint (averaged across all folds). Conclusion: The prediction performance of foot2hip is encouraging and shows its applicability in accurately predicting lower limb kinematics with minimal wearables. Significance: The hardware used along with foot2hip is low cost ($268 + N× $35, N is the number of different foot sizes), comfortable, and easy to use. Therefore, it is suitable for most clinical and personal applications

Gait assessment scores are used for quantifying the abnormalities in the gait. Evaluation of the performance of these scores is a must for their clinical acceptance. However, current methods of assessing the performance of the gait assessment scores for clinically relevant gait abnormalities are prone to error. For example, values of intra-observer reliability, inter-observer reliability and sensitivity calculated for a gait assessment score change with the population of patients and observers. Therefore, there is a need for a methodology for replicating musculoskeletal deformations such as contracture in healthy individuals for objectively evaluating the performance of gait assessment scores with variable severity of musculoskeletal deformations. In this study, a series of dynamic musculoskeletal simulations are performed to simulate and verify a mathematical model of a passive exoskeleton for simulating contractures. The proposed model achieved a root mean square error of 1.864° and a correlation of coefficient of 0.984 while testing on five unique combinations of linear and non-linear torques and seven degrees of severity of hamstring contracture. To understand the tolerance of the proposed model to environmental noises, its performance is also tested at various perturbations. The results indicate that a passive exoskeleton attached to an unimpaired musculoskeletal model can accurately simulate the contracture of the targeted muscles.

The nasal breathing parameters have shown promising applications in hyperventilation, Kussmaul breathing, sleep apnea, shortness of breath in COVID-19, bradypnea, sinus arrhythmia, and breathing pranayama. However, a wearable setup, which is affordable, aesthetic, and accurate in real-time during various physical activities, is not available currently. The present work develops a novel, low-cost and wearable instrumented Nasal Temperature Sensing (NTS) device for measuring various breathing parameters. The proposed device was tested during controlled breathing exercises in seated position at three ambient temperature settings (18°C, 28°C, and 38°C). The device was further tested for standing and walking at various speeds including slow (1.5 Km/hr), medium (3.5 Km/hr), and fast (5.5 Km/hr). The NTS device is compared against a commercially available respiration belt (gold standard). The mean absolute error (averaged across subjects and ambient temperature settings) was 0.09±0.05 breaths per minute (bpm), 0.17±0.10 bpm, and 0.13±0.13 bpm for controlled breathing at 6 bpm, 12 bpm, and 18 bpm, respectively. The Root-mean-square error between the NTS device and respiration belt across the subjects at the normal standing-position was 1.13±0.39 bpm. The results suggest that the proposed NTS device is accurate for real-time applications with the potential advantage of being robust enough to motion artifacts.

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

Neuromuscular disorders in Cerebral Palsy (CP) patients lead to foot deformities and affect foot biomechanics leading to compromised gait. Thus, measurement of the foot kinematic measurement is of particular interest to understand and characterize the walking pattern among CP patients. The objective of the present work is to develop a wearable instrument to measure foot kinematics such as foot-to-ground angle in three-dimensional planes and to measure the foot clearance i.e., toe and heel clearances. A template-based outsole was developed that incorporated an optical distance sensor located anatomically on the outsole and the magnetometer to measure the foot kinematics. The developed system was validated against the reference marker-based motion capture system (from Noraxon). The data from eight able-bodied participants were acquired simultaneously from both the systems (developed and the reference system) at three different walking speeds. A CoP based feedback was presented to the participants to shift the sagittal CoP anteriorly, posteriorly and normal to simulate the walking pattern of CP patients with three different foot landing strategies. Pearson's correlation coefficient of more than or equal to 0.62, root mean square error of less than or equal to 7.81 degrees and limit of agreement of more than or equal to 95% is found. Furthermore, a wireless wristband is developed and validated for real-time vibrotactile feedback. The measurement accuracy reported with outsole while participants simulated CP gait shows the potential of present work in real-time foot kinematics detection in CP patients. The instrumentation is wearable, low-cost, easy to use and implement.