Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle

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.

Sensors and algorithms for locomotion intention detection of lower limb exoskeletons

In recent years, lower limb exoskeletons (LLEs) have received much attention due to the potential to help people with paraplegia regain the ability of upright-legged locomotion. However, one major hindrance to converting prototypes into actual products is the lack of a balance recovery function. Locomotion intentions can be the first step for balance assistance. Therefore, its significance continues to grow. Many researchers focus on this topic, but there is a lack of a general discussion on the research phenomenon. Therefore, the purpose of this work is to systematize these data and benefit future research. This review is divided into two parts, the location of sensors/devices and the evaluation criteria of algorithms, which are the main components of locomotion intentions. We found that sensor/device placement is still concentrated in the lower limbs, but most researchers have found the importance of the chest. The peak power of the signal collected from the chest may be overestimated because it undergoes higher vertical velocity and acceleration during a rotation. However, despite the differences in peak power between the upper and lower back, high correlations were found for the tasks, especially from sitting to standing. Since peak power is based on vertical acceleration and velocity, it can be considered a metric that is more robust to changes in sensor location. Therefore, data acquisition from the chest is effective. In this paper, it is pointed out that sensors placed on the chest may have a tendency to change, as some researchers have realized in the field of locomotion intention recognition. In the evaluation criteria, we also found that deep learning algorithm (such as Back Propagation Artificial Neural Network (BPANN)) is outstanding, and Support Vector Machine (SVM) is the most cost-effective algorithm. In terms of accuracy, sensitivity, and specificity, BPANN achieved nearly 100%. SVM has different types; the best one achieves 98% accuracy, 100% sensitivity, and 100% specificity. But it also has 87.8% accuracy, which is not stable. Convolutional Neural Networks (CNN) can be used for image classification and have an accuracy of around 87%. Compared to the above two algorithms, CNN may have lower performance. Other algorithms also have higher accuracy, sensitivity, and specificity. These evaluation criteria, however, were not all ideal at the same time. Based on these results, we also point out the existing problems. In general, the application of these algorithms to LLE can contribute to its intention recognition, which can be helpful in balancing research. Finally, this can help make LLE more suitable for daily use.

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.

Foot Insole Pressure Distribution during the Golf Swing in Professionals and Amateur Players

There are numerous articles that study the ground reaction forces during the golf swing, among which only a few analyze the pressure pattern distributed on the entire surface of the foot. The current study compares the pressure patterns on the foot insoles of fifty-five golfers, from three different performance levels, playing swings with driver and 5-iron clubs in the driving range. Five swings were selected for each club. During each swing, ultra-thin insole sensors (4 sensors/cm2) measure foot pressure at the frequency of 100 Hz. To perform statistical analysis, insole sensors are clustered to form seven areas, with the normalized pressure of each area being our dependent variable. A video camera was used to label the five key instants of the swing. Statistical analysis demonstrates a significant difference between the pressure distribution pattern of the left and right feet for both driver and 5-iron. However, the pressure distribution pattern remains almost the same when switching the club type from 5-iron to driver. We have also observed that there are significant differences between the pattern of professionals and players with medium and high handicap. The obtained pattern agrees with the principle of weight transfer with a different behavior between the medial and lateral areas of the foot.

Does induced fatigue alter dynamic balance in athletes? A systematic review

OBJECTIVES

To determine the influence of induced fatigue on dynamic balance in healthy athletes.

DESIGN

Systematic review.

DATA SOURCES

PUBMED, MEDLINE, CINAHL, Sports Discus, and the Cochrane library from onset to May 28, 2019.

ELIGIBILITY CRITERIA

Eligible studies included any study examining the effects of induced-fatigue on dynamic balance, as measured by the SEBT/YBT, in healthy athletic populations. Studies with a low risk of bias were considered scientifically admissible for a best evidence synthesis.

RESULTS

Fifteen studies with low risk of bias were included - seven investigated recreational athletes while eight focused on competitive athletes. In the recreational population, five of the studies found significant decrease in dynamic balance following the fatiguing intervention. However, the remaining two concluded with insignificant changes. As for the competitive population, three studies showed significant effects of induced fatigue on dynamic balance, while five showed no effects.

CONCLUSION

There are conflicting results regarding the effects of induced fatigue on dynamic balance. The majority of studies focused on competitive athletes found that fatigue did not alter their dynamic balance. Per contra, the majority of studies focused on recreational athletes concluded the opposite - fatigue did indeed affect dynamic balance.

Deficits in the Star Excursion Balance Test and Golf Performance in Elite Golfers with Chronic Low Back Pain

The purpose of this study was to investigate whether low-handicap elite golfers with chronic low back pain (CLBP) exhibit deficits in dynamic postural control and whether CLBP affects golfers in terms of their golf swing parameters. A total of fifteen Division 1 college golfers were recruited as participants. Of these, six of whom experienced CLBP, while the remaining participants were healthy. In this study, CLBP was defined as experiencing chronic pain symptoms for more than six months. The Star Excursion Balance Test (SEBT) was administered to examine dynamic posture control in both groups. The TrackMan Golf Launch Monitor Simulator was used to collect data on the performance parameters of the swing of the participants. The results for both feet in the medial, lateral, posterior, posteromedial, and posterolateral directions indicated that the CLBP group scored lower than the control group. However, the CLBP group scored higher for the right foot in the anterolateral direction. The parameters for the club speed and ball carry of the CLBP group were lower than those of the control group. Further, the CLBP group exhibited a more upright swing plane relative to the control group. Taken together, our findings suggest that SEBT may be feasible and highly accessible to assess golf swing performance of elite players with CLBP.

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.

Effects of a Dual-Task Paradigm and Gait Velocity on Dynamic Gait Stability during Stair Descent

Falls during stair negotiation have become one of the leading causes of accidental death. The effects of a concurrent cognitive or manual dual-task paradigm on dynamic gait stability remain uncertain. How much dynamic gait stability is influenced by gait velocity is also not clear. A total of 16 healthy young females descended a staircase under three different walking conditions: descend stairs only (single task), descend stairs while performing subtraction (cognitive dual-task), and descend stairs while carrying a glass of water (manual dual-task). An eight-camera Vicon motion analysis system and a Kistler force plate embedded into the third step of the staircase were used synchronously to collect kinematic and kinetic data. Gait velocity decreased and dynamic gait stability increased with both cognitive and manual dual-task conditions. The center of mass–center of pressure inclination angle increased with gait velocity but decreased with the manual dual-task condition compared to the single-task condition. Changes in gait velocity caused by the dual-task paradigm can partially explain the effects of dual-task dynamic gait stability. The influence of gait velocity should be considered in the assessment of dual-task effects.

Physical Determinants of Golf Swing Performance: A Review

Sheehan, WB, Bower, RG, and Watsford, ML. Physical determinants of golf swing performance: A review. J Strength Cond Res XX(X): 000-000, 2019-Traditionally, golf practice has primarily focused on the mental, technical, and skill aspects as the primary means to improve performance. Only recently has a greater emphasis been placed on the physical components with balance, muscular strength, power, and specific muscle-tendon properties demonstrating positive associations with club head speed and carry distance. Accordingly, this review will explore the influence of these physical components on measures of golf swing performance. Superior balance may allow players to effectively deal with the need to shift weight during the swing as well as different stance positions, whereas superior lower-body muscular strength, power, and stiffness may allow more mechanical work to be performed on the club during the swing per unit of time, consequently increasing club head speed. Alternatively, flexibility may also contribute to enhanced force production with a greater range of motion, particularly when generating the "X-factor," allowing for a longer backswing and more time to produce higher angular velocities and forces. Furthermore, training intervention studies focusing on the aforementioned components have demonstrated enhancements in swing performance. Targeting multiple muscle groups, including those implicated via electromyography activation, and utilizing multiple modalities have proven effective at increasing club head speed. However, such multifaceted programs have made it difficult to determine the mechanisms that specifically contribute to performance gains. Despite these limitations, strength, power, and musculotendinous stiffness, particularly in the lower body, seem to be stronger determinants of club head speed and carry distance than flexibility. Furthermore, acute improvements can be induced using resistance-orientated warm-ups.

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.

The effect of uphill and downhill slopes on centre of pressure movement, alignment and shot outcome in mid-handicap golfers

The aim of the study was to examine changes in centre of pressure (COP) movement, alignment and shot outcome during golf shots from flat, uphill, and downhill slopes by mid-handicap golfers. Twelve male golfers hit balls with a six-iron from the flat and 5° slopes while kinematics and kinetics of the swing were collected. A launch monitor measured performance outcomes. A shift in the COP was found during the backswing when playing on a slope, but disappeared during the downswing. Golfers attempted to align the body perpendicular to the slope at the start of the swing resulting in COP movement towards the lower foot, but were not able to maintain this throughout the swing, like low handicap golfers. There was no significant difference in stance width, but golfers placed the ball closer to the uphill foot on a slope. Ball speed was not significantly affected by the slope, but launch angle and ball spin were. Golfers were more likely to hit shots to the left from an uphill slope and to the right for a downhill slope. No consistent compensatory adjustments in alignment at address were found, with differences in final ball position due to lateral spin.