Wearable Real-Time Haptic Biofeedback Foot Progression Angle Gait Modification to Assess Short-Term Retention and Cognitive Demand

Shull, Peter B.; Xia, Haisheng; Charlton, Jesse M.; Hunt, Michael A.

doi:10.1109/tnsre.2021.3110202

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…In this study, the participant population was limited to young adults to study the immediate effects of rhythmic feedback on arm swing and gait, while avoiding confounding factors such as the effects of aging on participants' performance. Recent limited results of using tactile haptic in improving the arm swing amplitude of a stroke patient [32] and the stride-time variability of PD patients [33] show promise and results from the literature support the use of haptic feedback given that synchronizing and reacting to such cues happen unconsciously [43] or without significantly increasing the user's cognitive load [49]. However, due to the different proprioceptive, cognitive, and physical abilities of older adults and other clinical populations, which may hinder sensing tactile feedback or adjusting gait accordingly, the immediate and long-term effects of the presented feedback system remain to be investigated in those populations.…”

Section: H Limitationsmentioning

confidence: 99%

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Noghani

Hossain

Hejrati

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Due to the neural coupling between upper and lower limbs and the importance of interlimb coordination in human gait, focusing on appropriate arm swing should be a part of gait rehabilitation in individuals with walking impairments. Despite its vital importance, there is a lack of effective methods to exploit the potential of arm swing inclusion for gait rehabilitation. In this work, we present a lightweight and wireless haptic feedback system that provides highly synchronized vibrotactile cues to the arms to manipulate arm swing and investigate the effects of this manipulation on the subjects' gait in a study with 12 participants (20-44 years). We found the developed system effectively adjusted the subjects' arm swing and stride cycle times by significantly reducing and increasing those parameters by up to 20% and 35%, respectively, compared to their baseline values during normal walking with no feedback. Particularly, the reduction of arms' and legs' cycle times translated into a substantial increase of up to 19.3% (on average) in walking speed. The response of the subjects to the feedback was also quantified in both transient and steady-state walking. The analysis of settling times from the transient responses revealed a fast and similar adaptation of both arms' and legs' movements to the feedback for reducing cycle time (i.e., increasing speed). Conversely, larger settling times and the time differences between arms' and legs' responses were observed due to feedback for increasing cycle times (i.e., reducing speed). The results clearly demonstrate the potential of the developed system to induce different arm-swing patterns as well as the ability of the proposed method to modulate key gait parameters through leveraging the interlimb neural coupling, with implications for gait training.

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Section: H Limitationsmentioning

confidence: 99%

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Noghani

Hossain

Hejrati

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…recurrent neural networks (RNN), heuristics, thresholds, support vector machine (SVM), reduced support vector machine (RSVM), finite state machine (FSM) algorithms. Real-time haptic biofeedback devices are implemented to correct toe-in or toe-out during walking, using foot progression angle gait algorithm [29]. Real-time algorithms, such as convolutional long short-term memory neural network (CLSTM-NN), heuristic and fast complementary filter (FCF) algorithms are widely studied for classification of gait terrain and walking modes, such as overground walking, stair ascend or descend and others [30], [31], [32].…”

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confidence: 99%

Novel Real-time In-step Gait Anomaly Detection Algorithms

Rostovski,

Krivošei,

Kuusik

et al. 2024

Preprint

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Goal: Real-time abnormality detection in gait analysis is an open challenge. The existing state-of-the-art is limited by the fact that it does not take into account ’in-step’ anomaly detection. Timely detection of gait abnormalities during the mid-swing phase of the step (i.e., within 50ms detection time) is required to make informed gait correction. To meet this requirement and overcome state of the art limitations, we present real-time anomaly detection algorithms, a new dataset, and a framework to estimate performance of anomaly detection algorithms. Methods: We propose real-time in-step anomaly detection algorithms namely, i) real-time tslearn support vector machines anomaly detection (RTtsSVM-AD), ii) real-time one class support vector machines anomaly detection (RTOCSVMAD), and iii) signal shape tracking anomaly detection (SSTAD). For comparative assessment, twenty-two healthy volunteers realistically simulated eight different characteristic deviations in human gait of certain lower extremity disabilities. Motion patterns were recorded using an inertial motion unit (IMU) placed on the forefoot. F1 score, recall, precision, real-time factor (RTF), as well as ”earliness” measures are estimated and analyzed. The ”earliness” is a new metric which defines the time between the beginning of a step and the moment in time when the step is classified as abnormal. Results: The achieved results demonstrate that the proposed algorithms can detect gait abnormalities in real-time during the mid-swing phase of an ongoing step. The average accuracy and F1 score achieved by the three algorithms are: 91% and 81% for SST-AD; 74% and 54.9% for RTOCSVM-AD; and 64.5% and 49.2% for RTtsSVMAD, respectively. The best average earliness is achieved by the SST-AD algorithm at 0.4 second from the initial-swing phase start. Conclusion: Based on the results, SST-AD is the best suited algorithm for real-time gait anomaly detection and should be considered to be used in future embedded assistive devices.

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Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Rostovski,

Ahmadilivani,

Krivošei

et al. 2024

Communications in Computer and Information Science

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Anomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.

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Wearable Real-Time Haptic Biofeedback Foot Progression Angle Gait Modification to Assess Short-Term Retention and Cognitive Demand

Cited by 7 publications

References 49 publications

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Modulation of Arm Swing Frequency and Gait Using Rhythmic Tactile Feedback

Novel Real-time In-step Gait Anomaly Detection Algorithms

Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

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