Depression is a common reason for an increase in suicide cases worldwide. Thus, to mitigate the effects of depression, accurate diagnosis and treatment are needed. An electroencephalogram (EEG) is an instrument used to measure and record the brain's electrical activities. It can be utilized to produce the exact report on the level of depression. Previous studies proved the feasibility of the usage of EEG data and deep learning (DL) models for diagnosing mental illness. Therefore, this study proposes a DL-based convolutional neural network (CNN) called DeprNet for classifying the EEG data of depressed and normal subjects. Here, the Patient Health Questionnaire 9 score is used for quantifying the level of depression. The performance of DeprNet in two experiments, namely, the recordwise split and the subjectwise split, is presented in this study. The results attained by DeprNet have an accuracy of 0.9937, and the area under the receiver operating characteristic curve (AUC) of 0.999 is achieved when recordwise split data are considered. On the other hand, an accuracy of 0.914 and the AUC of 0.956 are obtained, while subjectwise split data are employed. These results suggest that CNN trained on recordwise split data gets overtrained on EEG data with a small number of subjects. The performance of DeprNet is remarkable compared with the other eight baseline models. Furthermore, on visualizing the last CNN layer, it is found that the values of right electrodes are prominent for depressed subjects, whereas, for normal subjects, the values of left electrodes are prominent.

An exoskeleton needs reference joint angle profiles at various speeds and slopes for its application in real-world scenarios. Recording these profiles and their implementation in the control system of an exoskeleton are time-consuming and complex processes. Therefore, there is a need for an artificial system that can predict subject-specific joint angle profiles for various real-world scenarios from a minimum amount of input data. This study aims to propose a predictive neural network (NN) called MoveNet, for joints angle profile prediction across variable walking speeds and slopes. MoveNet consists of three NN modules, namely, encoder, mapper, and decoder. The encoder module is trained to convert the knee joint angle profile into its 6-D latent representation. The mapper module is trained to map the latent representation of angle profile at zero degrees angle of inclination (AOI) to latent representation of angle profile at the target AOI. The decoder module is trained to predict an angle profile from its given latent representation. The proposed model successfully predicted knee joint angle profiles at three walking speeds (0.8, 1, and 1.2 m/s) and nine AOI ranging from −10° to 10°. MoveNet obtained root mean squared error of 3.24 ± 1.19° and mean absolute error of 2.66 ± 1.00° from tenfold cross validation. These results suggest that artificial intelligence can predict subject-specific knee joint angle profiles for variable slopes at a given walking speed from the data of knee joint angle profiles recorded at a flat surface.

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.

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, especially in Cerebral Palsy patients, lead to foot deformities and affect foot biomechanics leading to compromised gait. Thus, the foot kinematic measurement is of particular interest to understand and characterize the walking pattern. The present work aims 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 optical distance sensors located anatomically on the outsole and a magnetometer to measure the foot kinematics. The developed system was validated against the reference marker-based motion capture system from Noraxon, USA. 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 center of pressure (CoP) based feedback was presented to the participants to shift the sagittal CoP anteriorly, posteriorly and medially to simulate the walking pattern of patients with the neuromuscular disorder at three different foot landing strategies. It was found that more than or equal to 96.48% values of the differences of both the systems is between the limit of agreement, values of Pearson’s correlation coefficient were more than or equal to 0.76 and values of root mean square error were less than or equal to 3.45 degrees. Furthermore, a wireless wristband was developed and validated for real-time vibrotactile feedback. Since instrumented outsole is wearable, low-cost, easy to use and implement, it has a great potential in real-time foot kinematic measurements.

The nasal breathing parameters have shown promising applications in hyperventilation, Kussmaul breathing, sleep apnea, shortness of breath in the 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 developed 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/h), medium (3.5 km/h), and fast (5.5 km/h). 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 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.