Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly

Studies of Acceleration of the Human Body during Overturning and Falling from a Height Protected by a Self-Locking Device

The use of individual fall protection equipment is one of the most commonly applied methods of protecting workers whose worksites are located above the floor level. The safety of the user in such a situation depends on both the proper selection and correct use of such equipment. Additionally, aspects such as minimizing the free-fall distance before the fall arrest, as well as quick notification of an accident and efficient rescue operation, are important factors influencing safety. This paper presents a new testing method for fall arrest equipment using a test stand consisting of the Hybrid III 50th Pedestrian ATD anthropomorphic manikin and measuring set with three-axis acceleration transducers. The proposed method and test stand were developed for the design and testing of new fall protection devices equipped with electronic detection and alarm systems, for which it is necessary to determine acceleration limits in order to determine the alarm threshold. The proposed method is based on the measurement of accelerations that occur during tipping and falling from the height of an anthropomorphic manikin secured by a self-locking device. Two places of attachment of the measuring set with a three-axis acceleration sensor were analyzed at the waist belt of the manikin (abdomen and back). Moreover, the self-locking device lanyard was attached to the two points of the safety harnesses (the front and back point). The aim of the research was to check whether the acceleration values depend on the places of attachment of the measuring and anchored system, as well as to determine their maximum values. Acceleration values corresponding to fall arrest and tipping were analyzed. Limits of acceleration have been established in order to determine the threshold of alarm triggering. The non-parametric Mann-Whitney U test was used to check whether the location of the three-axis acceleration transducer and the position of the self-locking device lanyard attachment affect the value of the recorded acceleration. For results of acceleration measurements when testing the behavior of the manikin during fall arrest, no statistically significant differences were found. For results of acceleration measurements when testing the tipping behavior of the manikin, statistically significant differences occurred. This means that during fall arrest, the location of the three-axis acceleration transducer and the position of the self-locking device lanyard attachment do not matter. This work is a continuation of previous research on accelerations characterizing human body positions occurring during normal physical activities (ADL-activities of daily living).

Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor

Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; $p<0.001$ ). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89–0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system.

Promotion of active ageing through interactive artificial agents in a smart environment

Abstract

Societies in the most developed countries have witnessed a significant ageing of the population in recent decades, which increases the demand for healthcare services and caregivers. The development of technologies to help the elderly, so that they can remain active and independent for a longer time, helps to mitigate the sustainability problem posed in care services. This article follows this new trend, proposing a multi-agent system composed of a smart camera network, centralised planning agent, a virtual coach, and robotic exercise buddy, designed to promote regular physical activity habits among the elderly. The proposed system not only persuades the users to perform exercise routines, but also guides and accompanies them during exercises in order to provide effective training and engagement to the user. The different agents are combined in the system to exploit their complementary features in the quest for an effective and engaging training system. Three variants of the system, involving either a partial set of those agents or the full proposed system, were evaluated and compared through a pilot study conducted with 12 elderly users. The results demonstrate that all variants are able to guide the user in an exercise routine, but the most complete system that includes a robotic exercise buddy was the best scored by the participants.

Article Highlights

WeMos IoT Controller-Based Low-Cost Fall Detection System for Elderly Users

Fall detection systems for the elderly are very important to protect this type of users. The early detection of the fall of the elderly has a major impact on saving their lives and avoiding the deterioration of the negative medical effects resulting from the effect of the patient falling on a hard surface. One of the constraints in fall detection systems are false-negative errors (no fall detection) or false-positive errors (sending a false warning without real fall accident). These errors have to be reduced significantly. In this paper, an innovative method to reduce fall detection system errors is proposed. The system consists of two orientation detection sensors to track the body orientation instead of using a single sensor in the previous systems which enhances the system accuracy and reduces the false-negative and false-positive errors. The system uses a small size IoT-based controller to process the sensor's information and make the alarm decision based on specific thresholds. The output alarm of the system includes an email sent to the caregivers via the embedded Wi-Fi ESP8266 module as well as an SMS message to the caregivers’ phones via GSM modules to ensure that the alarm message arrives in the absence of internet coverage for the patient or the caregiver. The system is powered by a small lithium-Ion battery. All sensors and modules of the system are combined in a small rubber box that can be fixed in a waist belt or the chest rejoin of the user body. Several tests have been made in different procedures. The tests revealed that the new approach improves the accuracy of the system and reduces the possibility of triggering wrong alarms.

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors' sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Fall Detection for the Elderly Based on 3-Axis Accelerometer and Depth Sensor Fusion with Random Forest Classifier

In this paper, we propose a new fall detection method that combines 3-axis accelerometer and depth sensors. By combining vision and acceleration-derived features we managed to minimize the false detection rate that is considerably higher when the decision is based on just one type of feature. Also, using machine learning has led to good generalization performance. In addition, we newly created fall database that are more realistic than previous ones. Experiment results show that the proposed method can efficiently detect falls.

Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor

Throughout a patient's stay in the Intensive Care Unit (ICU), accurate measurement of patient mobility, as part of routine care, is helpful in understanding the harmful effects of bedrest [1]. However, mobility is typically measured through observation by a trained and dedicated observer, which is extremely limiting. In this work, we present a video-based automated mobility measurement system called NIMS: Non-Invasive Mobility Sensor . Our main contributions are: (1) a novel multi-person tracking methodology designed for complex environments with occlusion and pose variations, and (2) an application of human-activity attributes in a clinical setting. We demonstrate NIMS on data collected from an active patient room in an adult ICU and show a high inter-rater reliability using a weighted Kappa statistic of 0.86 for automatic prediction of the highest level of patient mobility as compared to clinical experts.

Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis

Improving activity recognition, with special focus on fall-detection, is the subject of this study. We show that Kalman smoothed in-painting of missing pose information and task-specific dimensionality reduction of activity feature vectors leads to significantly improved activity classification performance. We illustrate our findings by applying common classification algorithms to dimensionally reduced feature vectors, and compare our accuracy to previous work. In part two we investigate our methods on a small subset of the data, in order to ascertain what accuracy performance is achievable with the smallest amount of information available.

Fall detection devices and their use with older adults: a systematic review

BACKGROUND

Falls represent a significant threat to the health and independence of adults aged 65 years and older. As a wide variety and large number of passive monitoring systems are currently and increasingly available to detect when individuals have fallen, there is a need to analyze and synthesize the evidence regarding their ability to accurately detect falls to determine which systems are most effective.

OBJECTIVES

The purpose of this literature review is to systematically assess the current state of design and implementation of fall-detection devices. This review also examines to what extent these devices have been tested in the real world as well as the acceptability of these devices to older adults.

DATA SOURCES

A systematic literature review was conducted in PubMed, CINAHL, EMBASE, and PsycINFO from their respective inception dates to June 25, 2013.

STUDY ELIGIBILITY CRITERIA AND INTERVENTIONS

Articles were included if they discussed a project or multiple projects involving a system with the purpose of detecting a fall in adults. It was not a requirement for inclusion in this review that the system targets persons older than 65 years. Articles were excluded if they were not written in English or if they looked at fall risk, fall detection in children, fall prevention, or a personal emergency response device.

STUDY APPRAISAL AND SYNTHESIS METHODS

Studies were initially divided into those using sensitivity, specificity, or accuracy in their evaluation methods and those using other methods to evaluate their devices. Studies were further classified into wearable devices and nonwearable devices. Studies were appraised for inclusion of older adults in sample and if evaluation included real-world settings.

RESULTS

This review identified 57 projects that used wearable systems and 35 projects using nonwearable systems, regardless of evaluation technique. Nonwearable systems included cameras, motion sensors, microphones, and floor sensors. Of the projects examining wearable systems, only 7.1% reported monitoring older adults in a real-world setting. There were no studies of nonwearable devices that used older adults as subjects in either a laboratory or a real-world setting. In general, older adults appear to be interested in using such devices although they express concerns over privacy and understanding exactly what the device is doing at specific times.

LIMITATIONS

This systematic review was limited to articles written in English and did not include gray literature. Manual paper screening and review processes may have been subject to interpretive bias.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

There exists a large body of work describing various fall-detection devices. The challenge in this area is to create highly accurate unobtrusive devices. From this review it appears that the technology is becoming more able to accomplish such a task. There is a need now for more real-world tests as well as standardization of the evaluation of these devices.

Simple fall criteria for MEMS sensors: data analysis and sensor concept

This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept.

A smart phone-based pocket fall accident detection, positioning, and rescue system

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

Simulated unobtrusive falls detection with multiple persons

One serious issue related to falls among the elderly living at home or in a residential care facility is the "long lie" scenario, which involves being unable to get up from the floor after a fall for 60 min or more. This research uses a simulated environment to investigate the potential effectiveness of using wireless ambient sensors (dual-technology (microwave/infrared) motion detectors and pressure mats) to track the movement of multiple persons and to unobtrusively detect falls when they occur, therefore reducing the rate of occurrence of "long lie" scenarios. A path-finding algorithm (A*) is used to simulate the movement of one or more persons through the residential area. For analysis, the sensor network is represented as an undirected graph, where nodes in the graph represent sensors, and edges between nodes in the graph imply that these sensors share an overlapping physical region in their area of sensitivity. A second undirected graph is used to represent the physical adjacency of the sensors (even where they do not overlap in their monitored regions). These graphical representations enable the tracking of multiple subjects/groups within the environment, by analyzing the sensor activation and adjacency profiles, hence allowing individuals/groups to be isolated when multiple persons are present, and subsequently monitoring falls events. A falls algorithm, based on a heuristic decision tree classifier model, was tested on 15 scenarios, each including one or more persons; three scenarios of activity of daily living, and 12 different types of falls (four types of fall, each with three postfall scenarios). The sensitivity, specificity, and accuracy of the falls algorithm are 100.00%, 77.14%, and 89.33%, respectively.

Evaluation of accelerometer-based fall detection algorithms on real-world falls

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans

Falls are the number one cause of injury in older adults. Wearable sensors, typically consisting of accelerometers and/or gyroscopes, represent a promising technology for preventing and mitigating the effects of falls. At present, the goal of such "ambulatory fall monitors" is to detect the occurrence of a fall and alert care providers to this event. Future systems may also provide information on the causes and circumstances of falls, to aid clinical diagnosis and targeting of interventions. As a first step towards this goal, the objective of the current study was to develop and evaluate the accuracy of a wearable sensor system for determining the causes of falls. Sixteen young adults participated in experimental trials involving falls due to slips, trips, and "other" causes of imbalance. Three-dimensional acceleration data acquired during the falling trials were input to a linear discriminant analysis technique. This routine achieved 96% sensitivity and 98% specificity in distinguishing the causes of a falls using acceleration data from three markers (left ankle, right ankle, and sternum). In contrast, a single marker provided 54% sensitivity and two markers provided 89% sensitivity. These results indicate the utility of a three-node accelerometer array for distinguishing the cause of falls.

Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors

Falls and their related injuries are a major challenge facing elderly people. One serious issue related to falls among the elderly living at home is the 'long-lie' scenario, which is the inability to get up from the floor after a fall, followed by lying on the floor for 60 minutes, or more. Several studies of accelerometer and gyroscope-based wearable falls detection devices have been cited in the literature. However, when the subject moves around at night-time, such as making a trip from the bedroom to the toilet, it is unlikely that they will remember or even feel an inclination to wear such a device. This research will investigate the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs), that will detect falls automatically by recognizing unusual activity sequences in the home environment; hence, decreasing the number of subjects suffering the 'long-lie' scenario after a fall. A Java-based wireless sensor network (WSN) simulation was developed. This simulation reads the room coordinates from a residential map, a path-finding algorithm (A*) simulates the subject's movement through the residential environment, and PIR and PM sensors respond in a binary manner to the subject's movement. The falls algorithm was tested for four scenarios; one scenario including activities of daily living (ADL) and three scenarios simulating falls. The simulator generates movements for ten elderly people (5 female and 5 male; age: 50-70 years; body mass index: 25.85-26.77 kg/m(2)). A decision tree based heuristic classification model is used to analyze the data and differentiate falls events from normal activities. The sensitivity, specificity and accuracy of the algorithm are 100%, 66.67% and 90.91%, respectively, across all tested scenarios.

Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components

This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patient's body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subject's visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patient's status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed.

A design of the u-health monitoring system using a Nintendo DS game machine

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

A wearable airbag to prevent fall injuries

We have developed a wearable airbag that incorporates a fall-detection system that uses both acceleration and angular velocity signals to trigger inflation of the airbag. The fall-detection algorithm was devised using a thresholding technique with an accelerometer and gyro sensor. Sixteen subjects mimicked falls, and their acceleration waveforms were monitored. Then, we developed a fall-detection algorithm that could detect signals 300 ms before the fall. This signal was used as a trigger to inflate the airbag to a capacity of 2.4 L. Although the proposed system can help to prevent fall-related injuries, further development is needed to miniaturize the inflation system.

Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention

Fall detection and prevention require logged physiological activity data of a patient for a long period of time. This work develops a data acquisition system to collect motion data from multiple patients and store in a data base. A wireless sensor network is built using high precision inertia sensors and low power Zigbee wireless transceivers. Testing results prove the system function properly. Researchers and physicians can now retrieve and analyze the accurate data of the patient movement with ease.

A fall and near-fall assessment and evaluation system

The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.

Distinguishing falls from normal ADL using vertical velocity profiles

This paper describes a technique for distinguishing falls from activities of daily living (ADL) through vertical velocity thresholding (VVT). To verify that VVT can be used to distinguish falls from ADL and to detect falls prior to impact, simulated fall and ADL testing was carried out on five young healthy subjects. Results show that the VVT method can distinguish falls from ADL with 100% accuracy and with an average lead-time of 323ms prior to trunk impact and 140ms prior to knee impact.

Fall detection--principles and methods

Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

Wireless connectivity for health and sports monitoring: a review

This is a review of health and sports monitoring research that uses or could benefit from wireless connectivity. New, enabling wireless connectivity standards are evaluated for their suitability, and an assessment of current exploitation of these technologies is summarised. An example of the application is given, highlighting the capabilities of a network of wireless sensors. Issues of timing and power consumption in a battery-powered system are addressed to highlight the benefits networking can provide, and a suggestion of how monitoring different biometric signals might allow one to gain additional information about an athlete or patient is made.