# US 2014/0156040 A1 · Apparatus and method for analyzing a golf swing

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- Number: US 2014/0156040 A1
- Document ID: US20140156040A1
- Status: Published application
- Kind: A1
- Application number: 14/174,294
- Filed: 2014-02-06
- Published: 2014-06-05
- Earliest claimed: 2007-11-05
- Inventors: Brian Francis Mooney
- Web page: https://patentgrep.com/patent/US20140156040A1

## CPC

- A63B 24/0006: {Computerised comparison for qualitative assessment of motion sequences or the course of a movement}
- A63B 69/36: for golf
- A63B 2069/367: {Detection of balance between both feet, i.e. weight distribution}
- A63B 2220/51: Force
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)

## Abstract

This invention is an apparatus and method for measuring or analyzing a golf swing. Measurement or analysis is made relative to energy generation and transfer through a player's body and club. The measurement or analysis data is principally obtained from the player's ground-reaction forces. Processed signals are analyzed with an artificial intelligence system. Ground-reaction forces relate to reaction forces which occur between a standing surface and the player's feet. The apparatus and method measures or analyses a golf swing in an automatic manner or in an automatic and interactive manner.

## Claims

### Claim at position 1

-140. (canceled)

### Claim 141

An apparatus for measuring or analysing a golf swing, comprising:
a processing means,
a detection means operable to detect ground-reaction forces,
a standing surface, and
a sensor means, characterised in that
a) the apparatus includes an artificial intelligence means;
b) the processing means includes an early-processing means, and information from the sensor means or detection means is processable by the early-processing means, into data which better characterises the swing, before being received by the artificial intelligence means; and
c) the artificial intelligence means is operable to receive and process information from the early-processing means.

### Claim 142 (depends on 141)

An apparatus according to claim 141, wherein
the sensor means senses load responses, rather than deformation responses to the standing surface; and
the sensor means comprises a plurality of sensors wherein some information from some of the plurality of sensors is processed separately from some information from other sensors.

### Claim 143 (depends on 141)

An apparatus according to claim 141, wherein
information from the sensor means or detection means is processable by the early-processing means, into data which better characterises the swing, before being received by the artificial intelligence means, by all or a combination of the following techniques:
a) smoothing of the data stream, such as the use of an arithmetic moving average;
b) scaling to ensure comparable reading between different sensors;
c) temperature stabilising, to overcome errors from changing temperatures;
d) voltage stabilising, to overcome errors from changing system voltages;
e) conversion to COP X and Y positions on individual feet or across a combination of both feet;
f) conversion to COP X and Y velocities on individual feet or across a combination of both feet; and
g) conversion to COP X and Y accelerations on individual feet or across a combination of both feet.

### Claim 144 (depends on 141)

An apparatus according to claim 141, wherein a selection is made from the following group:
the artificial intelligence means comprises one or more trained artificial neural networks;
separate networks are used to predict different energy-parameters;
separate networks are used to predict energy-parameters for different club types;
a neural network is a multi-layer feed-forward network with backward propagation and is trained with supervised learning;
a neural network comprises a single hidden layer with 30-70 neurons;
different types of networks are used to extract different types of energy-parameters.

### Claim 145 (depends on 144)

An apparatus according to claim 144, wherein
the network is a time-series-prediction network which predicts values of an application parameter as it varies across a chronological series of points in time, by training an output against the actual value of the training variable at each point of the series, the application parameter being predicted by a smoothed or fitted plot, or chronological succession, of the predicted application outputs.

### Claim 146 (depends on 145)

An apparatus according to claim 145, wherein
the network comprises all or at least one of the following features:—
a) smoothing is carried out by calculating a moving average of the predicted application outputs, for example an eleven point moving average;
b) fitting is carried out by fitting the predicted results to a polynomial, such as a third order polynomial;
c) a combination of smoothing and fitting is used, with fitting used at peaks or inflexions in the plot or chronological succession;
d) a peak is fitted for values over a set percentage;
e) training inputs are obtained from detection means processed data;
f) training outputs for kinematic data are obtained using high-accuracy motion capture methods; and
g) training outputs for kinetic energy, local energy generation, latching and unlatching are obtained using high-accuracy motion capture methods together with computer android models.

### Claim 147 (depends on 145)

An apparatus according to claim 145, wherein
a time-series-prediction network is operable to predict a parameter from the following selection of energy-parameters across the swing or relevant portions of the swing:
a) magnitudes of segment and sub-segment local energy/forces activations;
b) segment linear and angular kinetic energy levels;
c) absolute speeds of body and club segments, including club head absolute speed;
d) angular and linear positions, velocities and accelerations of body and club segments through the swing, due to displacement by the local muscle group;
e) angular and linear positions, velocities and accelerations of body and club segments through the swing; and
f) angular positions, velocities and accelerations between the trunk and arm segments and between the arm and club segments.

### Claim 148 (depends on 144)

An apparatus according to claim 144, wherein
the network is a time-point-prediction network which predicts an application point in time for an event against a succession of chronologically occurring application values, by training a training output against a fuzzy definition of the training input, whereby when application inputs are subsequently applied to the trained network, the application point in time is predicted where a peak value occurs on a smoothed plot, or chronological succession, of the predicted application outputs.

### Claim 149 (depends on 148)

An apparatus according to claim 148, wherein
the network comprises all or at least one of the following features:
a) the fuzzy definition of the training input is calculated as a value which varies with distance of the training input point from the actual point of the training input, decreasing in value with increasing distance from the actual point;
b) the fuzzy definition is based on a triangular weighting function, where the training input value has a cut-off distance from the actual value of the training input, and where intermediate training input values reduce in proportion to the distance from the actual training value;
c) smoothing is carried out by calculating a moving average of the predicted application outputs, for example an eleven point moving average;
d) training inputs are obtained from detection means processed data;
e) training outputs for kinematic data are obtained using high-accuracy motion capture methods; and
f) training outputs for kinetic energy, local energy generation, latching and unlatching are obtained using high-accuracy motion capture methods together with computer android models.

### Claim 150 (depends on 148)

An apparatus according to claim 148, wherein
a time-point-prediction network is operable to predict a parameter from the following selection of energy-parameters:
a) times of start and completion of segment and sub-segment local energy/forces ramp-ups and ramp-downs;
b) times of latching and unlatching between connecting segments and sub-segments;
c) times of top-of-backswing events for body and club segments;
d) times of maximum muscle stretch-shortening between the various connecting segments;
e) times of local energy generation peaks in segments, sub-segments and club head;
f) times of angular/linear velocity and acceleration peaks in segments, sub-segments and club head;
g) times of auxiliary frontal-plane energy transfer centre-of-pressure velocity and acceleration peaks; and
h) times of commencement and termination of auxiliary frontal-plane characteristics.

### Claim 151 (depends on 145)

An apparatus according to claim 145, wherein
inputs or outputs are represented as normalised values; and
peaks and inflexions in position or velocity plots or chronological sequences are calculated by integrating acceleration or velocity predictions, respectively.

### Claim 152 (depends on 145)

An apparatus according to claim 145, wherein
a plot or chronological succession of values is predicted by a combination of time-series-prediction and time-point-prediction, with the time-point-prediction used to predict particular features of the plot or chronological succession of values; and where a particular feature is a peak or inflexion in the plot or chronological succession of values.

### Claim 153 (depends on 144)

An apparatus according to claim 144, wherein
the network is a compressed-data-prediction network which predicts a value or parameter which requires information from across a swing or from portions of a swing, or which requires information on aspects of a swing which related to more than one point or a narrow portion of a swing;
inputs associated with a swing are processed into compressed functions and used as training inputs against a training output, the trained network predicting the application output by applying application compressed functions as application inputs.
the network comprises a combination of the following features:
a) the compressed functions are Fourier transforms or wavelet transforms;
b) training inputs comprise processed data from the detection means;
c) training inputs comprise data from other networks, including time-series-predicted data; and
d) training outputs include data measured by motion capture systems; data from android computer modelling; and expert human observation and analysis of the player, the player's swing and the player's centre-of-pressure data.

### Claim 154 (depends on 153)

An apparatus according to claim 153, wherein
a compressed-data-prediction network is operable to predict a parameter from the following selection of energy-parameters:
a) category of swing type, related to auxiliary frontal-plane energy generation and transmission;
b) player's body weight;
c) category of player's body type;
d) category or type of club played;
e) times of impact and takeaway;
f) durations between the components of related time events, including durations between top-of-backswing between connecting segments, durations between segment peak kinetic energies and durations between local energy activations;
g) categories of peak or inflexion normalised shapes occurring at specific events in time-series plots or chronological sequences; and
h) scaling factors for normalised values predicted by other networks, including angular and linear positions, velocities and accelerations, forces, kinetic energies and local energies and scaling factors for auxiliary frontal-plane energy generation and transmission characterising events.
and wherein
compressed-data-prediction is used to predict all or most of the energy-parameters of the swing.

### Claim 155 (depends on 141)

An apparatus according to claim 141, where the detection means is operable to detect vertical and side forces, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the network include all or a selection from the following:
a) X, Y and Z forces from each of the sensor positions;
b) centre-of-pressure position in the X direction for the left foot, the right foot and for the combination of both feet;
c) centre-of-pressure position in the Y direction for the left foot, the right foot and for the combination of both feet;
d) centre-of-pressure velocity in the X direction for the left foot, the right foot and for the combination of both feet;
e) centre-of-pressure velocity in the Y direction for the left foot, the right foot and for the combination of both feet;
f) centre-of-pressure acceleration in the X direction for the left foot, the right foot and for the combination of both feet; and
g) centre-of-pressure acceleration in the Y direction for the left foot, the right foot and for the combination of both feet.

### Claim 156 (depends on 141)

An apparatus according to claim 141, where the detection means is operable to detect vertical forces, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the network include all or a selection from the following:
a) Z forces from each of the sensor positions;
b) centre-of-pressure position in the X direction for the left foot, the right foot and for the combination of both feet;
c) centre-of-pressure position in the Y direction for the left foot, the right foot and for the combination of both feet;
d) centre-of-pressure velocity in the X direction for the left foot, the right foot and for the combination of both feet;
e) centre-of-pressure velocity in the Y direction for the left foot, the right foot and for the combination of both feet;
f) centre-of-pressure acceleration in the X direction for the left foot, the right foot and for the combination of both feet; and
g) centre-of-pressure acceleration in the Y direction for the left foot, the right foot and for the combination of both feet.

### Claim 157 (depends on 155)

An apparatus according to claim 155, wherein
the detection means network inputs are used to predict reference times in the swing, such as takeaway and impact using a compressed-data-prediction.
and wherein
reference times in the swing, such as takeaway and impact, are used to construct a time-marker input, which can associate a normalised number to each time instant in the swing.

### Claim 158 (depends on 155)

An apparatus according to claim 155, where the detection means is operable to detect vertical and side forces, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the network must include the following:
a) a time-marker;
b) X, Y and Z forces from each of the sensor positions;
c) centre-of-pressure velocity in the X direction for the non-target-side foot;
d) centre-of-pressure velocity in the Y direction for the non-target-side foot;
e) centre-of-pressure position in the X direction for the combination of both feet;
f) centre-of-pressure position in the Y direction for the combination of both feet;

### Claim 159 (depends on 156)

An apparatus according to claim 156, where the detection means is operable to detect vertical forces, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the network include all or a selection from the following:
a) a time-marker;
b) Z forces from each of the sensor positions;
c) centre-of-pressure velocity in the X direction for the non-target-side foot;
d) centre-of-pressure velocity in the Y direction for the non-target-side foot;
e) centre-of-pressure position in the X direction for the combination of both feet;
f) centre-of-pressure position in the Y direction for the combination of both feet;

### Claim 160 (depends on 158)

An apparatus according to claim 158, where the network is a time-series-prediction network or time-point-prediction network, wherein
the inputs include a selection of at least one from the following inputs predicted by compressed-data-prediction:
a) club type;
b) swing type related to auxiliary frontal-plane energy generation and transmission; and
c) player body type identification.

### Claim 161 (depends on 141)

An apparatus according to claim 141, wherein
centre-of-pressure data are used without network prediction, either from the processed outputs from the detection means or calculated by the processor from these outputs, centre-of-pressure data including centre-of-pressure positions in time, magnitudes, velocities, accelerations and lengths of displacement; and
wherein
energy-parameters are prepared for human presentation, for example for use by experts employed in devising automatic analysis of the data or for direct use by coaches for analysis of a player's swing.

### Claim 162 (depends on 141)

An apparatus according to claim 141, wherein
energy-parameters are automatically analysed or evaluated using all or a selection from the following techniques:
a) evaluation or analysis in light of the optimisation-rules;
b) evaluation or analysis by comparison to the swings of expert players;
c) evaluation or analysis by use of a relative noisiness method;
d) evaluation or analysis by comparison to other swings by the same player; and
e) evaluation and analysis on a health safety basis.

### Claim 163 (depends on 162)

An apparatus according to claims 162, where energy-parameters are automatically analysed or evaluated in light of the optimisation-rules, wherein the optimisation rules include those relating to:
a) optimum set-up of the top-of-backswing of segments;
b) optimum magnitude and timings of local energy generation in segments;
c) optimum latching and launching of segments;
d) optimum transfer of energy through swing and flail transfer to the club head; and
e) optimum timing of peak club head speed.

### Claim 164 (depends on 162)

An apparatus according to claim 162, where energy-parameters are automatically analysed or evaluated by comparison to the swings of expert players, wherein the analysis or evaluations includes a selection from the following features:
a) comparison to the relevant energy-parameters of the equivalent swing or swing range of an appropriate expert model, the expert model being based on a synthesis of swings by expert players, adjusted to be appropriate to the swing and player under analysis;
b) comparison is made to expert traits in energy parameters, including the timing and varying magnitudes of local energy generation, the manner in which segments are unlatched and launched, and the timed mechanics of the more distal swing and flail mechanisms; and
c) comparison is made to a synthesis where errors are eliminated and expert traits, as most commonly displayed by experts, are retained, the synthesis being adjusted to allow for the player's body type and body weight, the basis for such adjustment being determined from study of the experts themselves, where wide ranges of body type and weight exist.

### Claim 165 (depends on 162)

An apparatus according to claim 162, where energy-parameters are automatically analysed or evaluated by use of a relative noisiness method, where the method relates to analysing the noise level of a predicted network outputs for a swing, or portion of a swing, and inferring better performance with reducing noise level; and
wherein the analysis or evaluations includes a selection from the following features:
a) comparison is made to the noisiness level of a reference swing or other reference value;
b) comparison is made to a reference swing based on the play of expert players;
c) levels of noisiness are established as measures of goodness of fit or quality of fit of the raw output data to smoothed output data; and
d) analysis or evaluation is used to highlight relative weaknesses or strengths at different threshold levels across the swing.

### Claim 166 (depends on 162)

An apparatus according to claim 162, where energy-parameters are automatically analysed or evaluated by comparison to other swings by the same player, wherein the analysis or evaluations includes all or a selection from the following comparisons:
a) comparison to a player's history of previous swings;
b) comparison to an immediate series of swings with the same club; and
c) comparison to swings carried out with other clubs.

### Claim 167 (depends on 162)

An apparatus according to claim 162, where energy-parameters are automatically analysed or evaluated on a health safety basis, wherein the analysis or evaluations includes all or a selection from the following comparisons:
a) identification of potential risks of injury inherent in a player's existing swings; and
b) identification of potential risks of injury which might arise from attempted changes in a player's energy generation and transmission.

### Claim 168 (depends on 162)

An apparatus according to claim 162, wherein a selection is made from the following group:
energy-parameters are analysed in conjunction with external apparatus or systems, including additional sensing means, which provide further information on the swing;
energy-parameters are prepared for human presentation, including use by coaches or players for analysis of a player's swing;
the system is operable to provide evaluation or analysis which does not require further human analysis or interpretation;
the processing means comprises an electronic processor.
the apparatus comprises an interactive training means and a communication means, wherein
the interactive training means communicates with the processing means to provide automatic interactive training to a player; the interactive training means is operable to prompt a training element and the communication means is operable to communicate the training element to the player;
interactive training elements are pre-prepared by experts familiar with such energy generation and transfer within the swing, with how they can be improved and how improvement can be effectively communicated to a player;
the processing means is operable to process the energy-parameters from sensory information from the detection means, process information from the artificial intelligence means, analyse the results, process the interactive training routines and communicate with the communication means;
the training data means comprises means which are operable to provide training data to the processor, and include a selection from data storage, retrieval and transmission devices including internet connections, CD and DVD readers and electronic memory, both external and within the system;
the communication means includes means which allow the apparatus to communicate with the player or coach, including visual display screens and wireless audio receivers, the communication means also including means which allow the player or coach to communicate with the apparatus, including visual touch screens and keyboards.

### Claim 169 (depends on 141)

An apparatus according to claim 141, wherein a selection is made from the following group:
the detection means is a force plate or pressure pad;
the detection means is a force plate which is operable to measure vertical and side ground-reaction forces;
the detection means is a force plate or pressure pad which is operable to measure only vertical ground-reaction forces;
the detection means comprises two platforms or pad sections, which are operable to separately measure ground-reaction forces for the players left and rights feet.

### Claim 170

A method for measuring or analysing a golf swing using ground-reaction forces, the method comprising the steps of:
a) obtaining ground-reaction force information during the swing;
b) processing the information into data which better characterises the swing; and
c) receiving and processing the processed data by artificial intelligence.

### Claim 171 (depends on 170)

A method according to claim 170, wherein
ground-reaction force information is obtained as load response information, rather than deformation response information; and
some of the information is processed separately from other information.

### Claim 172 (depends on 170)

A method according to claim 170, wherein
information is processed into data which better characterises the swing, before being received and processed by artificial intelligence, by all or a combination of the following techniques:
a) smoothing of the information stream, such as the use of an arithmetic moving average;
b) scaling to ensure comparable information values;
c) temperature stabilising, to overcome errors from changing temperatures;
d) voltage stabilising, to overcome errors from changing system voltages;
e) conversion to centre-of-pressure X and Y positions on individual feet or across a combination of both feet;
f) conversion to centre-of-pressure X and Y velocities on individual feet or across a combination of both feet; and
g) conversion to centre-of-pressure X and Y accelerations on individual feet or across a combination of both feet.

### Claim 173 (depends on 170)

A method according to claim 170, wherein a selection is made from the following group:
the artificial intelligence comprises use of trained artificial neural network methods;
separate networks are used to predict different energy-parameters;
separate networks are used to predict energy-parameters for different club types'
neural network methods comprise multi-layer feed-forward network methods with backward propagation and trained with supervised learning;
neural network methods comprise use of a single hidden layer with 30-70 neurons;
different types of networks are used to extract different types of energy-parameters.

### Claim 174 (depends on 144)

A method according to claim 144, wherein
the network produces a time-series-prediction which predicts values of an application parameter as it varies across a chronological series of points in time, by training an output against the actual value of the training variable at each point of the series, the application parameter being predicted by a smoothed or fitted plot, or chronological succession, of the predicted application outputs.

### Claim 175 (depends on 174)

A method according to claim 174, wherein
the network comprises all or at least one of the following features:
a) smoothing is carried out by calculating a moving average of the predicted application outputs, for example an eleven point moving average;
b) fitting is carried out by fitting the predicted results to a polynomial, such as a third order polynomial;
c) a combination of smoothing and fitting is used, with fitting used at peaks or inflexions in the plot or chronological succession;
d) a peak is fitted for values over a set percentage;
e) training inputs are obtained from detection means processed data;
f) training outputs for kinematic data are obtained using high-accuracy motion capture methods; and
g) training outputs for kinetic energy, local energy generation, latching and unlatching are obtained using high-accuracy motion capture methods together with computer android models.

### Claim 176 (depends on 174)

A method according to claim 174, wherein
a time-series-prediction is operable to predict a parameter from the following selection of energy-parameters across the swing or relevant portions of the swing:
a) magnitudes of segment and sub-segment local energy/forces activations;
b) segment linear and angular kinetic energy levels;
c) absolute speeds of body and club segments, including club head absolute speed;
d) angular and linear positions, velocities and accelerations of body and club segments through the swing, due to displacement by the local muscle group;
e) angular and linear positions, velocities and accelerations of body and club segments through the swing; and
f) angular positions, velocities and accelerations between the trunk and arm segments and between the arm and club segments.

### Claim 177 (depends on 173)

A method according to claim 173, wherein
the network produces a time-point-prediction which predicts an application point in time for an event against a succession of chronologically occurring application values, by training a training output against a fuzzy definition of the training input, whereby when application inputs are subsequently applied to the trained network, the application point in time is predicted where a peak value occurs on a smoothed plot, or chronological succession, of the predicted application outputs.

### Claim 178 (depends on 177)

A method according to claim 177, wherein
the network method comprises all or at least one of the following features:
a) the fuzzy definition of the training input is calculated as a value which varies with distance of the training input point from the actual point of the training input, decreasing in value with increasing distance from the actual point;
b) the fuzzy definition is based on a triangular weighting function, where the training input value has a cut-off distance from the actual value of the training input, and where intermediate training input values reduce in proportion to the distance from the actual training value;
c) smoothing is carried out by calculating a moving average of the predicted application outputs, for example an eleven point moving average;
d) training inputs are obtained from detection means processed data;
e) training outputs for kinematic data are obtained using high-accuracy motion capture methods; and
f) training outputs for kinetic energy, local energy generation, latching and unlatching are obtained using high-accuracy motion capture methods together with computer android models.

### Claim 179 (depends on 177)

A method according to claim 177, wherein
a time-point-prediction is operable to predict a parameter from the following selection of energy-parameters:
a) times of start and completion of segment and sub-segment local energy/forces ramp-ups and ramp-downs;
b) times of latching and unlatching between connecting segments and sub-segments;
c) times of top-of-backswing events for body and club segments;
d) times of maximum muscle stretch-shortening between the various connecting segments;
e) times of local energy generation peaks in segments, sub-segments and club head;
f) times of angular/linear velocity and acceleration peaks in segments, sub-segments and club head;
g) times of auxiliary frontal-plane energy transfer centre-of-pressure velocity and acceleration peaks; and
h) times of commencement and termination of auxiliary frontal-plane characteristics.

### Claim 180 (depends on 174)

A method according to claim 174, wherein
inputs or outputs are represented as normalised values, and
peaks and inflexions in position or velocity plots or chronological sequences are calculated by integrating acceleration or velocity predictions, respectively.

### Claim 181 (depends on 174)

A method according to claim 174, wherein
a plot or chronological succession of values is predicted by a combination of time-series-prediction and time-point-prediction, with the time-point-prediction used to predict particular features of the plot or chronological succession of values; and where a particular feature is a peak or inflexion in the plot or chronological succession of values.

### Claim 182 (depends on 173)

A method according to claim 173, wherein
a compressed-data-prediction predicts a value or parameter which requires information from across a swing or from portions of a swing, or which requires information on aspects of a swing which related to more than one point or a narrow portion of a swing;
inputs associated with a swing are processed into compressed functions and used as training inputs against a training output. The trained network predicts the application output by applying application compressed functions as application inputs;
and wherein
the method comprises a combination of the following features:
a) the compressed functions are Fourier transforms or wavelet transforms;
b) training inputs comprise processed data from the detection means;
c) training inputs comprise data from other networks, including time-series-predicted data; and
d) training outputs include data measured by motion capture systems; data from android computer modelling; and expert human observation and analysis of the player, the player's swing and the player's centre-of-pressure data.

### Claim 183 (depends on 182)

A method according to claim 182, wherein
a compressed-data-prediction is operable to predict a parameter from the following selection of energy-parameters:
a) category of swing type, related to auxiliary frontal-plane energy generation and transmission;
b) player's body weight;
c) category of player's body type;
d) category or type of club played;
e) times of impact and takeaway;
f) durations between the components of related time events, including durations between top-of-backswing between connecting segments, durations between segment peak kinetic energies and durations between local energy activations;
g) categories of peak or inflexion normalised shapes occurring at specific events in time-series plots or chronological sequences; and
h) scaling factors for normalised values predicted by other networks. These include angular and linear positions, velocities and accelerations. They also include, forces, kinetic energies and local energies. They further include scaling factors for auxiliary frontal-plane energy generation and transmission characterising events.

### Claim 184 (depends on 182)

A method according to claim 182, wherein
compressed-data-prediction is used to predict all or most of the energy-parameters of the swing.

### Claim 185 (depends on 170)

A method according to claim 170, where vertical and side forces are detected, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the artificial intelligence or networks include all or a selection from the following:
a) X, Y and Z forces components of the different sensed signals;
b) centre-of-pressure position in the X direction for the left foot, the right foot and for the combination of both feet;
c) centre-of-pressure position in the Y direction for the left foot, the right foot and for the combination of both feet;
d) centre-of-pressure velocity in the X direction for the left foot, the right foot and for the combination of both feet;
e) centre-of-pressure velocity in the Y direction for the left foot, the right foot and for the combination of both feet;
f) centre-of-pressure acceleration in the X direction for the left foot, the right foot and for the combination of both feet; and
g) centre-of-pressure acceleration in the Y direction for the left foot, the right foot and for the combination of both feet.

### Claim 186 (depends on 163)

A method according to claim 163, where vertical forces are detected, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the artificial intelligence or networks include all or a selection from the following:
a) Z forces from the sensed signals;
b) centre-of-pressure position in the X direction for the left foot, the right foot and for the combination of both feet;
c) centre-of-pressure position in the Y direction for the left foot, the right foot and for the combination of both feet;
d) centre-of-pressure velocity in the X direction for the left foot, the right foot and for the combination of both feet;
e) centre-of-pressure velocity in the Y direction for the left foot, the right foot and for the combination of both feet;
f) centre-of-pressure acceleration in the X direction for the left foot, the right foot and for the combination of both feet; and
g) centre-of-pressure acceleration in the Y direction for the left foot, the right foot and for the combination of both feet;
and wherein
the inputs to the artificial intelligence or networks are used to predict reference times in the swing, such as takeaway and impact using a compressed-data-prediction.

### Claim 187 (depends on 186)

A method according to claim 186, wherein
reference times in the swing, such as takeaway and impact, are used to construct a time-marker input, which can associate a normalised number with each time instant in the swing.

### Claim 188 (depends on 185)

A method according to claim 185, where vertical and side forces are detected, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the artificial intelligence or networks must include the following:
a) a time-marker;
b) X, Y and Z force components of the sensed signals;
c) centre-of-pressure velocity in the X direction for the non-target-side foot;
d) centre-of-pressure velocity in the Y direction for the non-target-side foot;
e) centre-of-pressure position in the X direction for the combination of both feet; and
f) centre-of-pressure position in the Y direction for the combination of both feet.

### Claim 189 (depends on 186)

A method according to claim 186, where vertical forces are detected, with mutually orthogonal directions referred to as X, Y and Z directions, Z being vertical, wherein
the processed inputs to the artificial intelligence or networks include all or a selection from the following:
a) a time-marker;
b) Z forces from the sensed signals;
c) centre-of-pressure velocity in the X direction for the non-target-side foot;
d) centre-of-pressure velocity in the Y direction for the non-target-side foot;
e) centre-of-pressure position in the X direction for the combination of both feet; and
f) centre-of-pressure position in the Y direction for the combination of both feet.

### Claim 190 (depends on 188)

A method according to claim 188, where the prediction is a time-series-prediction or time-point-prediction, wherein
the inputs include a selection of at least one from the following inputs predicted by compressed-data-prediction:
a) club type;
b) swing type related to auxiliary frontal-plane energy generation and transmission; and
c) player body type;
and wherein
centre-of-pressure data are used without network prediction, either from the processed outputs from the detection information or calculated by processing these outputs, centre-of-pressure data including centre-of-pressure positions in time, magnitudes, velocities, accelerations and lengths of displacement.

### Claim 191 (depends on 163)

A method according to claim 163, wherein
energy-parameters are prepared for human presentation, for example for use by experts employed in devising automatic analysis of the data or for direct use by coaches for analysis of a player's swing.

### Claim 192 (depends on 163)

A method according to claim 163, wherein
energy-parameters are automatically analysed or evaluated using all or a selection from the following techniques:
a) evaluation or analysis in light of the optimisation-rules;
b) evaluation or analysis by comparison to the swings of expert players;
c) evaluation or analysis by use of a relative noisiness method;
d) evaluation or analysis by comparison to other swings by the same player; and
e) evaluation and analysis on a health safety basis.

### Claim 193 (depends on 192)

A method according to claim 192, where energy-parameters are automatically analysed or evaluated in light of the optimisation-rules, wherein the optimisation rules include those relating to:
a) optimum set-up of the top-of-backswing of segments;
b) optimum magnitude and timings of local energy generation in segments;
c) optimum latching and launching of segments;
d) optimum transfer of energy through swing and flail transfer to the club head; and
e) optimum timing of peak club head speed.

### Claim 194 (depends on 192)

A method according to claim 192, where energy-parameters are automatically analysed or evaluated by comparison to the swings of expert players, wherein the analysis or evaluations includes a selection from the following features:
a) comparison to the relevant energy-parameters of the equivalent swing or swing range of an appropriate expert model, the expert model being based on a synthesis of swings by expert players, adjusted to be appropriate to the swing and player under analysis;
b) comparison is made to expert traits in energy parameters, including the timing and varying magnitudes of local energy generation, the manner in which segments are unlatched and launched, and the timed mechanics of the more distal swing and flail mechanisms; and
c) comparison is made to a synthesis where errors are eliminated and expert traits, as most commonly displayed by experts, are retained, the synthesis being adjusted to allow for the player's body type and body weight, the basis for such adjustment being determined from study of the experts themselves, where wide ranges of body type and weight exist;
and where energy-parameters are automatically analysed or evaluated by use of a relative noisiness method, where the method relates to analysing the noise level of a predicted network outputs for a swing, or portion of a swing, and inferring better performance with reducing noise level.

### Claim 195 (depends on 194)

A method according to claim 194, wherein the analysis or evaluations includes a selection from the following features:
a) comparison is made to the noisiness level of a reference swing or other reference value;
b) comparison is made to a reference swing based on the play of expert players;
c) levels of noisiness are established as measures of goodness of fit or quality of fit of the raw output data to smoothed output data; and
d) analysis or evaluation is used to highlight relative weaknesses or strengths at different threshold levels across the swing.

### Claim 196 (depends on 192)

A method according to claim 192, where energy-parameters are automatically analysed or evaluated by comparison to other swings by the same player, wherein the analysis or evaluations includes all or a selection from the following comparisons:
a) comparison to a player's history of previous swings;
b) comparison to an immediate series of swings with the same club; and
c) comparison to swings carried out with other clubs.

### Claim 197 (depends on 192)

A method according to claim 192, where energy-parameters are automatically analysed or evaluated on a health safety basis, wherein the analysis or evaluations includes all or a selection from the following comparisons:
a) identification of potential risks of injury inherent in a player's existing swings; and
b) identification of potential risks of injury which might arise from attempted changes in a player's energy generation and transmission.

### Claim 198 (depends on 192)

A method according to claim 192, wherein a selection is made from the following group:
energy-parameters are analysed in conjunction with externally obtained information, including additional information sensed on the swing;
energy-parameters are prepared for human presentation, including use by coaches or players for analysis of a player's swing;
the system is operable to provide evaluation or analysis which does not require further human analysis or interpretation;
information is processed in an interactive manner, and the method is operable to prompt a training element and communicate the training element to the player;
interactive training elements are pre-prepared by experts familiar with such energy generation and transfer within the swing, with how they can be improved and how improvement can be effectively communicated to a player; and
sensory information is processed; the artificial intelligence obtains the energy-parameters from the processed information; the energy-parameters are processed to analyse or evaluate the swing; and interactive training routines are processed and communicated to a user, such as a player or coach.

### Claim 199 (depends on 198)

A method according to claim 198, wherein
interactive training includes provision of training data, including a selection from sources including data storage, data retrieval and data transmission, including internet sources, CD and DVD sources, and external and internal memory sources; and
communication to a user, such as a player or coach, includes visual and audio methods, and communication by a user includes touch-screen and keyboard methods.

### Claim 200 (depends on 170)

A method according to claim 170, wherein a selection is made from the following group:
ground-reaction forces are sensed resulting from loads applied by the feet of the player;
vertical and side ground-reaction forces are sensed;
only vertical ground-reaction forces are sensed;
ground-reaction forces are separately sensed or measure for the player's left and right feet.