Biomechanical Evaluation of Dynamic Balance Control Ability During Golf Swing

Effect of slope change on kinematics of amateur golfers' full swing

BACKGROUND

Golf courses are designed with uneven terrain. These factors are especially important when facing (slope), players need to straighten the posture of each part of the body in order to complete the swing on an inclined surface such as flat ground. Amateur players may be more likely to change the movement patterns of their shots due to uneven terrain. Therefore, it may be necessary to clarify the shot characteristics of amateur players and provide reference materials for technical improvement.

OBJECTIVE

The purpose of this study was to examine the effect of slope on amateur golfers' swing kinematics by analyzing the variation of time variables, body center of gravity (COG), and shot parameters of amateur golfers' swing at different ground slopes.

METHODS

Six male amateur golfers participated in the experiment. The 7-iron was used for 5 swings each at three slopes: flat ground (FG, 0∘), ball below foot (BBF, +10∘), and foot below ball (FBB, -10∘). The OptiTrack-Motion capture system was used to collect kinematic data, and the three-dimensional motion data will be transmitted to Visual3D software for subsequent data analysis such as golf swing division and body COG changes. Shot parameters (carry, swing speed, ball speed, and smash factor) were recorded for each swing using the Caddie SC300 radar monitoring device.

RESULTS

The results showed that there was no difference in the overall swing time and the time required for each interval at different slopes (p> 0.05) there is no significant difference in the change of the COG of the body in the forward and backward directions (p> 0.05). The three slopes of swing speed, ball speed, carry and smash factor were not significantly different (p> 0.05).

CONCLUSION

The rhythm of the amateur golfer's swing was not affected by the slope, but the slope restricts the movement of the body's COG, which may affect the weight movement, and ultimately cause the performance parameters to not reach the level of the FG.

Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit

Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.

A novel optimization-based convolution neural network to estimate the contribution of sensory inputs to postural stability during quiet standing

Adequate postural control is maintained by integrating signals from the visual, somatosensory, and vestibular systems. The purpose of this study is to propose a novel convolutional neural network (CNN)-based protocol that can evaluate the contributions of each sensory input for postural stability (calculated a sensory analysis index) using center of pressure (COP) signals in a quiet standing posture. Raw COP signals in the anterior/posterior and medial/lateral directions were extracted from 330 patients in a quiet standing with their eyes open for 20 seconds. The COP signals augmented using jittering and pooling techniques were transformed into the frequency domain. The sensory analysis indices were used as the output information from the deep learning models. A ResNet-50 CNN was combined with the k-nearest neighbor, random forest, and support vector machine classifiers for the training model. Additionally, a novel optimization process was proposed to include an encoding design variable that can group outputs into sub-classes along with hyperparameters. The results of optimization considering only hyperparameters showed low performance, with an accuracy of 55% or less and F-1 scores of 54% or less in all models. However, when optimization was performed using the encoding design variable, the performance was markedly increased in the CNN-classifier combined models (r = 0.975). These results suggest it is possible to evaluate the contribution of sensory inputs for postural stability using COP signals during a quiet standing. This study will facilitate the expanded dissemination of a system that can quantitatively evaluate the balance ability and rehabilitation progress of patients with dizziness.

Golf as a Physical Activity to Potentially Reduce the Risk of Falls in Older Adults with Parkinson's Disease

Advanced age is associated with an increased risk for falls in aging adults. Older adults are also more likely to be diagnosed with Parkinson’s disease (PD), with advanced age as the most significant risk factor. PD is a neurodegenerative disorder with four Cardinal motor symptoms: rigidity, bradykinesia, postural instability, and tremor. Thus, people (person)-with-Parkinson’s disease (PwP) have an even greater risk of falling than non-disorder age-matched peers. Exercise is an activity requiring physical effort, typically carried out to sustain or improve overall health and fitness, and it lowers the risk of falls in the general population. The sport of golf provides a low-impact all-around workout promoting a range of motion, activation of muscles in the upper and lower body, flexibility, and balance. Swinging a golf club offers a unique combination of high amplitude axial rotation, strengthening postural musculature, coordination, and stabilization, demonstrating the potential to impact PD symptoms positively. Golf may be a novel exercise treatment regimen for PD to use in conjunction with traditional medical therapy. We completed a literature review to determine the relationship between the game of golf, PD, and the risk of falls. We concluded that regularly playing golf can lower the risk for falls in community ambulating older adults with PD and demonstrates the potential to improve quality of life for PwP.

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall's impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

Machine learning approach to predict center of pressure trajectories in a complete gait cycle: a feedforward neural network vs. LSTM network

Center of pressure (COP) trajectories of human can maintain regulation of forward progression and stability of lateral sway during walking. The insole pressure system can only detect COP trajectories of each foot during single stance. In this study, we developed artificial neural network models that could present COP trajectories in an integrated coordinate system during a complete gait cycle using pressure information of the insole system. A feed forward artificial neural network (FFANN) and a long short-term memory (LSTM) model were developed. For FFANN, among 198 pressure sensors from Pedar-X insoles, proper input variables were selected using sequential forward selection to reduce input dimension. The LSTM model used all 198 signals as inputs because of its self-learning characteristic. As results of cross-validation, the FFANN model showed correlation coefficients of 0.98-0.99 and 0.93-0.95 in anterior/posterior and medial/lateral directions, respectively. For the LSTM model, correlation coefficients were similar to those of FFANN. However, the relative root mean square error (12.5%) of the FFANN model was higher than that (9.8%) of the LSTM model in medial/lateral direction (p = 0.03). This study can be used for quantitative evaluation of clinical diagnosis and rehabilitation status for patient with various diseases through further training using varied databases. Graphical abstract Architectures of neural networks developed in this study (a feed forward artificial neural network; b LSTM network).

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking

A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.