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. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: Biomech Model Mechanobiol. 2019 Nov 30;19(3):1003–1014. doi: 10.1007/s10237-019-01267-6

An Envelope of Linear and Rotational Head Motion During Everyday Activities

Logan E Miller 1,2, Jillian E Urban 1,2, Vincent M Whelan 3, Walt W Baxter 3, Stephen B Tatter 1, Joel D Stitzel 1,2
PMCID: PMC7210075  NIHMSID: NIHMS1544957  PMID: 31786677

Abstract

Various studies have characterized head kinematics in specific everyday activities by looking at linear and/or rotational acceleration characteristics, but each has evaluated a limited number of activities. Furthermore, these studies often present dissimilar and sometimes incomplete descriptions of the resulting kinematics, so the characteristics of normal everyday activities as a whole are not easily collectively summarized. The purpose of this study was to evaluate literature investigating head kinematics associated with everyday activities, and to generate a comprehensive kinematic boundary envelope describing these motions. The envelope constructed constitutes the current state of published knowledge regarding ‘normally occurring’ head accelerations. The envelope of kinematics represents activities commonly encountered and posing zero to minimal risk of injury to healthy individuals. Several kinematic measures, including linear accelerations, rotational velocities, and rotational accelerations, one may encounter as a result of normal everyday activities are summarized. A total of 11 studies encompassing 49 unique activities were evaluated. Examples of activities include sitting in a chair, jumping off a step, running and walking. The peak resultant linear accelerations of the head reported in the literature were all less than 15 g, while the peak resultant rotational accelerations and rotational velocities approach 1,375 rad/s2 and 12.8 rad/s, respectively. The resulting design envelope can be used to understand the range of acceleration magnitudes a typical active person can expect to experience. The results are also useful to compare to other activities exposing the head to motion or impact including sports, military, automotive, aerospace and other sub-injurious and injurious events.

Keywords: Brain injury, finite element model, strain, daily head accelerations, kinematics

1. Introduction

Brain injury occurs in a wide range of scenarios and severities. Concussion, also known as mild traumatic brain injury (mTBI), can occur in contact sports such as football and soccer, while more severe injuries like diffuse axonal injury (DAI) are more commonly seen in motor vehicle crashes. Although head impact exposure has been characterized in a range of activities known to produce brain injury, there is relatively little known about the kinematics associated with everyday activities. It is important to know the range of kinematics the brain is exposed to under normal, everyday circumstances to understand and establish injury thresholds, and may have applications to other fields including medical device design.

One area that has been extensively studied to investigate head impact biomechanics is football. The advent of impact sensing devices has allowed real-time data collection of on-field head impacts to quantify head impact exposure. The Head Impact Telemetry (HIT) System (Simbex, Lebanon, NH) has allowed direct instrumentation of football helmets to collect real-time linear and rotational acceleration data. Head kinematics associated with brain injury in football have been described at various levels of play. Rowson et al. (2013) reported concussive events have average accelerations of 104 ± 30g and 4,726 ± 1,931 rad/s2 at the collegiate level and 98 ± 28g and 6,432 ± 1,813 rad/s2 at the professional level (Rowson and Duma 2011, 2013). In addition to concussive events, athletes are exposed to a large number of subconcussive impacts during regular play, which are potentially biomechanically and clinically relevant (McKee et al. 2016; Mez et al. 2017). Studies report that a single athlete may sustain up to 1,400 impacts during a single season of football (Greenwald et al. 2008; Crisco et al. 2010). In general, we know that head impact exposure increases with increasing levels of play and that there is considerable variability among athletes. More specifically, subconcussive impacts are associated with mean acceleration values of 25.0g, 25.9g, and 22.3g and 1,121 rad/s2, 1,695 rad/s2, and 1,355 rad/s2 at the youth, high school, and collegiate levels, respectively (Rowson et al. 2009; Broglio et al. 2013; Kelley et al. 2017b). Other factors associated with variability in head impact exposure include level of play, team, position and time of season (Broglio et al. 2013; Campolettano et al. 2016; Kelley et al. 2017b, a; Urban et al. 2018).

To establish tolerance thresholds, various injury metric functions have been developed based on head kinematics. Two metrics commonly used in safety standards, the head injury criterion (HIC) and the severity index (SI), were both derived from the Wayne State Tolerance Curve (WSTC), which related linear acceleration and duration of acceleration to injury tolerance. Because rotational acceleration is believed to be the primary mechanism for brain injury, metrics based on rotational kinematics are likely more predictive of injury (King et al. 2003). The hypothesis that rotation of the brain, as opposed to translation, produces brain injury was first introduced in 1943 by Holbourn who hypothesized that the brain undergoes large deformations when subjected to rotation due to its low shear modulus (Holbourn 1943). This response has been observed in live humans during normal head motions as well as for higher severity impacts in human cadavers (Bayly et al. 2005; Hardy et al. 2007). Additionally, cerebral concussion has been observed in primates from rotational acceleration, and more severe injuries were observed when rotation was combined with translational contact – possibly as a result of coupled pressure gradients and diffuse strain (Ommaya and Gennarelli 1974). Rotation has also been shown to be important in injury prediction through experiments using various animal models as well as through finite element (FE) modeling, which have demonstrated that rotational kinematics strongly influence intracranial distortional strains (Ommaya and Gennarelli 1974; Gennarelli et al. 1982; Gennarelli 1983; Ommaya 1985; Margulies et al. 1990; King et al. 2003; Zhang et al. 2004; Kleiven 2006, 2007; Davidsson et al. 2009).

Despite the large body of work focused on subconcussive impacts, particularly in athletic environments, there is relatively little known about the brain’s tolerance to impacts or motion encountered during everyday activity. This could be in part due to disagreement in data collection methodologies between prior human tolerance studies. In particular, there is disagreement about what constitutes a subconcussive impact, or an impact that does not result in the signs and symptoms of concussion. Accordingly, all impacts or motion of the head that do not result in concussion are candidates to be classified as subconcussive impacts. It makes sense, however, that there is a lower limit to the severity of impacts or motions one would label subconcussive and that limit might be defined by the upper range of normal exposure encountered in everyday activities and tolerated well physiologically by every human being for the duration of their lives.

The current study aims to summarize and describe head kinematics of normal everyday activities through the creation of an envelope which may be used for design, descriptive or comparative purposes. This envelope is derived from head kinematics previously reported in the literature associated with everyday activities of an active person and constitutes ‘normally occurring’ head accelerations. Everyday activities considered in the current study may be differentiated from activities of daily living (ADL), which is a medical definition or classification referring to a set of activities and basic skills required to take care of one’s own body. ADLs include self-care tasks such as bathing/showering, dressing, eating, feeding, and functional mobility (Foti and Koketsu 2013). These tasks are not considered as most would likely result in negligible head kinematics. These data establish an envelope of head kinematics commonly encountered that pose minimal risk of injury to ordinary people.

2. Methods

A literature review was conducted to characterize head kinematics associated with everyday activities of a healthy active person. PubMed, an online literature database, was queried for peer-reviewed publications related to head kinematic response to everyday activities. Key search terms included ‘head acceleration’, 'daily activities’, ‘everyday activities’, and ‘non-injurious'. Key contributors and common authors were identified and searched within PubMed by author name to identify any additional publications that were not captured in the initial search criteria. Studies were considered eligible if they met the following criteria: (1) experimental tests on subjects instrumented with a device to measure head kinematics, (2) investigated non-injurious everyday activities, and (3) reported linear or rotational head acceleration peak values. For studies involving activities occurring during recreation or recreational sports, such as roller coasters, soccer headers, football impacts, or lacrosse impacts, associated activities were excluded from the review. Abstract-only publications, commentaries, anecdotal reports, and case studies were not considered.

Study design and methodology, subject characteristics, head sensor instrumentation, activities evaluated, and head kinematic measurements collected were examined. Repeated activities were tabulated from the studies examined in the literature review. Everyday activities were summarized with head kinematics including peak linear and rotational accelerations, peak rotational velocity, and rotational pulse duration for resultant curves. Additionally, when reported in the literature, kinematic curve peaks in the local X-, Y-, and Z-coordinate directions were recorded as well. The local coordinate system and directions of rotation are displayed in Fig. 1a. In a given study, if multiple tests were performed for a single activity, the average of all runs conducted was computed and reported for that activity.

Fig. 1.

Fig. 1

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Schematic displaying three coordinate directions (a) and assumed shape for the rotational acceleration pulse (b)

If rotational pulse duration was not reported in a study, it was calculated through integration using the reported peak value and a prescribed curve shape. In the current investigation, a half-sine pulse shape is assumed for the rotational acceleration pulse, as shown in Fig. 1b. This shape was selected because it is commonly used throughout the literature as a way to represent simplified head acceleration pulses (Chou and Nyquist 1974; Kimpara et al. 2006; Ji and Zhao 2015; Gabler et al. 2016; Zhao and Ji 2016). This half-sine curve is represented by the following equation:

α(t)=αPsin(πΔtt) (1)

where αP is the peak rotational acceleration in rad/s2 and Δt is the pulse duration (in ms). Integrating Equation 1 over the length of the pulse duration (0 – Δt) results in an equation for peak rotational velocity in terms of the pulse duration (Equation 2), which can be used to solve for Δt (in ms) as shown in Equation 3 (Chou and Nyquist 1974).

ωP=2αPΔtπ (2)
Δt=πωP2αP∗1000 (3)

where Δt is pulse duration (in ms), ωP is rotational velocity (in rad/s), and αP is rotational acceleration (in rad/s2).

The data from the examined studies were used to develop an envelope describing head acceleration and duration regimes for everyday activities. This envelope was defined using the relationship between rotational acceleration and rotational duration, which was plotted with duration on the Y axis and acceleration on the X axis. Curves to enclose the data were developed by manually selecting 7 boundary-defining data points along both the upper and lower extents of the data. Piecewise interpolation was performed using the PCHIP function in MATLAB to describe curves defining the upper and lower bounds of the data. Additionally, a curve representing the midline of the data was calculated as the midpoint of the upper and lower curves.

3. Results

The initial search criteria resulted in 31 published research studies. Exclusion of studies that did not report head kinematics or considered potentially injurious activities narrowed this down to five articles. The search for additional publications from key contributors and common authors expanded the literature search to a total of 11 articles included in the final analysis.

In each of the examined studies, subjects were instrumented with linear accelerometers and/or angular rate sensors affixed to the head using equipment such as biteplates or helmets. Subjects were then monitored while completing various activities. Detailed instrumentation information and sensor specifications are included in Appendix A. A summary of the data reported from each published study is provided in Table 1. Six of the examined studies considered exclusively everyday activities and looked at a total of 27 unique activities, 11 of which were investigated by at least two different research groups (Table 2). In the remaining five studies, one or more activities were excluded per study, as they were deemed not likely to occur during everyday activity (Table 3). Examples of excluded activities were roller coasters, soccer headers, diving onto a mat, and an 18 mph car crash. There were 22 remaining unique activities from these studies, bringing the total to 49 unique activities investigated in the 11 studies examined for the current study.

Table 1.

Details from Studies Reviewed

Reference # of
subjects		 Subject
ages		 # of
activities* 		Linear
Acceleration		 Rotational
Acceleration
XYZ Res Dur XYZ Res Dur
Allen et al. (1994) 8 19-50 13 ✓ ✓
Exponent (2002) 9 14-41 5 ✓ ✓
Kavanagh et al. (2004) 16 19-77 1 ✓
Ng et al. (2005) 18 19-32 7 ✓
Bussone (2005) 18 19-32 13 ✓ ✓ ✓ ✓
Pfister et al. (2009) 4 11-27 1 ✓ ✓
Bussone and Duma (2009) 18 19-32 8 ✓
Funk et al. (2011) 20 26-58 6 ✓ ✓
Bussone and Prange (2014) 8 2-7 17 ✓ ✓
Kuo et al. (2017) 2 26 1 ✓ ✓
Carriot et al. (2014) 8 22-34 11 ✓ ✓
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*

included in current analysis

Table 2.

Repeated Activities

Activity Allen et al. (1994) Exponent (2002) Kavanagh et al. (2004) Ng et al. (2005) Bussone (2005) Pfister et al. (2009) Bussone and Duma (2009) Funk et al. (2011) Bussone and Prange (2014) Kuo et al. (2017) Carriot et al. (2014)
1 look left ✓ ✓ ✓
2 stand up ✓ ✓
3 head nod ✓ ✓ ✓ ✓
4 sit down ✓ ✓ ✓ ✓
5 sneeze ✓ ✓
6 hop off step ✓ ✓ ✓ ✓ ✓
7 plop in chair ✓ ✓ ✓ ✓ ✓
8 walk ✓ ✓ ✓ ✓ ✓ ✓
9 run ✓ ✓ ✓ ✓ ✓ ✓
10 jumping jacks ✓ ✓
11 vertical leap ✓ ✓ ✓ ✓
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Table 3.

Excluded Activities

Reference Activities
Exponent (2002) pillow strike, falling down
Pfister et al. (2009) roller coaster, pillow fight, 18 mph car crash
Bussone and Prange (2014) diving onto mat
Funk et al. (2011) soccer header
Kuo et al. (2017) roller coaster, soccer header
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3.1. Kinematics from Published Studies

Of the kinematics of interest (linear acceleration, rotational acceleration, rotational velocity, and pulse duration), linear acceleration is the most commonly reported value – included for all activities in 10 of the 11 examined studies. Bussone and Duma (2009) is the only study that did not report linear acceleration. Pulse duration, however, was not directly reported in any of the studies examined – instead it was estimated using equations 1-3 and ranged from 14.6 to 104.9 ms. The distribution of resultant linear acceleration values for the 10 studies that reported linear acceleration is shown in Fig. 2; linear acceleration ranged from 0.32 to 13.8 g for the 49 unique activities considered.

Fig. 2.

Fig. 2

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Resultant linear acceleration for activities from all 11 studies

There were a total of 11 activities that were investigated in at least two studies (Table 2). Two of these activities, walking and running, were examined by 6 of the 11 studies considered. Linear acceleration and rotational acceleration are compared in Fig. 3a and b, respectively, for studies examining walking and running.

Fig. 3.

Fig. 3

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Comparison of linear acceleration (a) and rotational acceleration (b) for walking and running and linear acceleration (c) and rotational acceleration (d) for the common activities between the five studies listed in the figure legend

Five of the studies report linear accelerations about the three component axes. Fig. 4 shows the linear acceleration results from those four studies (Allen et al. 1994; Exponent 2002; Kavanagh et al. 2004; Bussone 2005; Carriot et al. 2014). A total of 44 activities are displayed in this figure.

Fig. 4.

Fig. 4

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X, Y, Z, and resultant linear acceleration results from Allen et al. (1994), Exponent (2002), Kavanagh et al. (2004), Bussone (2005) and Carriot et al. (2014)

Six of the 11 studies reported rotational acceleration (Bussone 2005; Bussone and Duma 2009; Funk et al. 2011; Bussone and Prange 2014; Kuo et al. 2017). Linear acceleration is plotted versus rotational acceleration in Fig. 5a for all corresponding activities. Rotational acceleration ranged from 13 to 1,375 rad/s2. For the studies that reported rotational kinematics, we can also examine rotational velocity, which ranged from 0.7 to 12.8 rad/s for the corresponding activities. Fig. 5b shows the relationship between rotational velocity and rotational acceleration for all activities examined by the six studies listed above. Fig. 5 demonstrates a trend for rotational acceleration to cluster along a 100:1 ratio of linear acceleration. Additionally, linear acceleration and rotational velocity tend to be on the same order as one another, as displayed by the similarity between Fig. 5a and b.

Fig. 5.

Fig. 5

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Linear vs. rotational acceleration (a) and rotational velocity vs. rotational acceleration (b) for activities from six of the examined studies. Lines of constant duration (10 – 100 ms) are also displayed in b. *Linear acceleration was not available for activities from Bussone and Duma (2009), so all corresponding data is plotted on the x axis

Fig. 6a shows the range of rotational acceleration, rotational duration, and linear acceleration values for the five studies that report both linear and rotational acceleration (Bussone 2005; Pfister et al. 2009; Funk et al. 2011; Bussone and Prange 2014; Kuo et al. 2017).

Fig. 6.

Fig. 6

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Rotational duration vs. rotational acceleration (a) and rotational duration vs. rotational acceleration with upper and lower bounds defining impact kinematics associated with everyday activities (b). Lines of constant rotational velocity (2 – 12 rad/s) are displayed in (a). Additionally, the range of rotational acceleration and pulse durations associated with the three distinct loading regimes are identified in (b). The size of the marker indicates the magnitude of linear acceleration for the respective activity

The envelope defining the upper and lower bounds for rotational acceleration and duration is shown in Fig. 6b. The upper bound was created by manually selecting points that approximated the upper boundary of the data. Similarly, points were sampled along the opposite boundary of the data to create the lower bound. The dashed line represents the approximate midline of the data.

4. Discussion

A literature review was conducted to characterize head kinematics associated with everyday activities of an active person. Care was taken to gather from the literature the head kinematics for common daily activities, such as sitting in a chair or a head nod. The current study aims to summarize head kinematics that fall below the lower bound on subconcussive events of interest – in other words, head motions which a person could be exposed to any amount of without being at risk of deleterious effects. Kinematic data from a total of 11 studies, representing 49 unique activities are summarized in the current study to establish an envelope of typical loading regimes of everyday activities. All but one of the studies examined, Bussone and Duma (2009), reported linear acceleration, and five of the 11 studies reported rotational kinematics. The largest peak linear and rotational acceleration values reported in the 11 studies examined were 19.7 g and 1,375 rad/s2, respectively, which were both associated with the ‘run and jump’ activity.

Results from Bussone and Prange (2014) clearly stand out, reporting much larger kinematic values for both walking and running compared to the other studies. This is likely due to the younger participant age; participants in this study were between the ages of 2 and 7, while all other studies considered the adult population (ages ranging from 19 to 77 years old) (Bussone and Prange 2014). For the remaining studies, linear acceleration did not exceed 3 g’s or 85 rad/s2 during either walking or running.

Other than walking and running, the two most commonly repeated activities were head nod and sitting down, which were both examined by five separate studies. Similarly, ‘hop off step’ and ‘plop in chair’ were frequently examined throughout the literature, with four of the studies reviewed here considering both activities. Kinematic measures for these four common activities are compared in Fig. 3c and d by study. Two of the references with results for the repeated activities only reported linear acceleration (Allen et al. 1994; Ng et al. 2005). Looking at the responses for the ‘head nod’ activity, the results reported by Funk et al. (2011) are approximately double those observed by Allen et al. (1994). This is likely due to differences in how the participants were instructed. For example, comparing the descriptions of the ‘head nod’ activity for the three studies that reported linear acceleration, three different movements were described. The participants in the study by Allen et al. (1994) were asked to ‘passively drop head backwards as if falling asleep,’ those participating in the 2005 study by Bussone were directed to ‘quickly nod head down and back up,’ and finally, volunteers in the study conducted by Funk et al. (2011) performed multiple ‘head shakes in succession’ in the sagittal plane. The inconsistencies in this activity’s description between studies likely accounts for the observed kinematic differences – particularly for the data reported by Funk et al. (2011). The kinematics associated with this activity were significantly larger in the study conducted by Funk et al. (2011) which can possibly be explained by the fact that the participants completed multiple anterior-posterior head motions in succession, which may have resulted in greater effort or exertion by the volunteers. Fig. 3 shows that the results from the 1994 study by Allen et al., which resulted in larger linear accelerations for ‘sit down,’ ‘stair jump,’ and ‘chair plop’ than all other studies that investigated these activities. This is likely attributed to differences in participant effort or technique rather than instruction or material differences in the step or chair (Bussone 2005).

An envelope encompassing the range of head kinematics experienced during everyday activities was developed using the relationship between pulse duration and rotational acceleration magnitude (Fig. 6b). This envelope acts as a constraint on the relationship between two important impact parameters: rotational acceleration and pulse duration. Simply defining expected ranges for these parameters would not be sufficient to characterize the kinematics associated with typical daily activities. For example, multiple activities resulted in either large accelerations or large durations, but the upper limits of both variables are associated with very small values of the other variable. Fig. 6b can be broken into three kinematic regimes: (1) low rotational acceleration and high duration, (2) low acceleration and low duration, and (3) high acceleration and low duration. Loading regime 1 encompasses rotational acceleration magnitudes up to approximately 65 rad/s2 and includes rotational durations exceeding 55 ms, which consists of activities such as ‘head nod,’ ‘normal sit in chair,’ and ‘look left’. The second regime spans rotational accelerations from approximately 90 to 600 rad/s2 and durations from 15 to 55 ms, and includes activities such as ‘stair jump,’ ‘chair plop,’ and ‘head shake’. The final loading regime contains acceleration magnitudes greater than 600 rad/s2 and rotational durations less than 25 ms, and includes ‘running,’ ‘jump rope,’ and ‘leaping’ activities. In general, the activities falling in these three regimes move from relatively mild (head nod, normal sit in chair) to more dynamic (chair plop, leap).

The mean/maximum linear and rotational acceleration associated with everyday activities was 3.8/13.8g and 287/1,375 rad/s2, respectively. Both the average and maximum values are values are much lower than the linear acceleration values associated with head impacts in football at the youth, high school, and collegiate levels: 25.0g, 25.9g, and 22.3g, respectively (Rowson et al. 2009, p. 20; Broglio et al. 2013; Kelley et al. 2017a). Comparing rotational acceleration, only the mean value associated with everyday activities is lower than the kinematics associated with youth, high school, and collegiate football head impacts: 1,121 rad/s2, 1,695 rad/s2, and 1,355 rad/s2, respectively. Although the maximum rotational acceleration associated with everyday activities was 1,375 rad/s2, only a total of 3 everyday activities had rotational acceleration magnitudes greater than 1,000 rad/s2. Fig. 7a compares linear and rotational accelerations associated with the everyday activities summarized in the current study to those collected during youth football. The youth football data was obtained as part of the ongoing Imaging Telemetry And Kinematic modeLing (iTAKL) study at Wake Forest; data collection methods have been previously reported (Urban et al. 2013; Kelley et al. 2017b). Also shown in Fig. 7a are curves representing various levels of injury risk based on the combined probability function (Rowson and Duma 2013). These risk curves show that while the football impacts span the whole range of risk thresholds shown, the accelerations associated with daily activities are well below 0.1% risk of injury (Fig. 7a). Even the activity generating the highest risk (run and jump) was only associated with a 0.02% risk of injury. Fig. 7b again compares the kinematics resulting from everyday activities to youth football. Curves representing various levels of injury risk based on rotational acceleration are shown as well as injury thresholds proposed by various studies (Ommaya 1985; Margulies and Thibault 1992; Davidsson et al. 2009; Rowson et al. 2012). Again, we note that none of the daily activities considered exceed 0.1% risk of injury based on their rotational acceleration values. Similar to the risk predicted by the combined probability, run and jump again resulting in the highest risk based solely on rotational acceleration with a risk of 0.006%. It is important to note, however, that high rotational velocity is more indicative of injury than high rotational acceleration since it takes pulse duration into account (Kleiven 2007). While curves indicating injury risk based only on linear kinematics are not shown in Fig. 7, predicting injury risk based only on linear acceleration values is an important consideration since this measure is frequently used to define thresholds for collecting on-field head impact data (10 g threshold is typical). As demonstrated by Fig. 2, only the following 4 everyday activities exceed the 10 g threshold: head strike, run and jump, jump rope and leap. These activities correspond to risks of 0.0097%, 0.0101%, 0.0104%, and 0.0112%, respectively (Rowson and Duma 2011). One potential application of the kinematic envelope derived in the current study is using the kinematic ranges to inform and update thresholds used for head injury biomechanics work.

Fig. 7.

Fig. 7

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Linear and rotational acceleration associated with everyday activities compared to those collected from youth football athletes (a) and rotational kinematics associated with everyday activities compared to those collected from youth football athletes (b). Additionally, published thresholds for DAI and published risk curves are included for reference (Rowson et al. 2012; Rowson and Duma 2013)

Limitations of the current study are related to the combination of data collected from different populations and using different data collection methods. There were a variety of ages in the sampled studies that spanned from pediatric subjects (ages 2-7), through the adult population, to elderly subjects (age 77) (Kavanagh et al. 2004; Pfister et al. 2009; Bussone and Prange 2014). As more studies measuring head kinematics during everyday activities are conducted and more data is gathered, distinctions between daily accelerations experienced by different age groups (e.g., children, young adults, elderly) may emerge. The envelope developed in the current study can be used as a framework for understanding the differences in everyday accelerations throughout the lifespan and motivates further research to understand the loads experienced during everyday activities. While the current study considers everyday activities as a helpful metric to compare to injurious impact exposure, we do not know the true threshold that may be detrimental with repeated and frequent long-term exposure. Additionally, various head acceleration measurement systems were used in the studies examined. These systems include helmet-mounted systems, headband-mounted systems, biteplate systems, as well as instrumented mouthguards. There are limitations associated with each type of measurement system. Helmet-mounted systems are limited by slip between the helmet and skull as well as noise from the helmet response. Additionally, the sensor mounting method can significantly affect the added mass to the head – for example a football helmet (~1.5kg) on an average human head (~4.5 kg) adds 33% mass. Accelerometers attached to the periphery of the head or mounted at other locations on the head also suffer from relative motion problems. Biteplates are limited by the ability of the jaw to maintain the plate in a constant position. There are also limitations associated with data processing in some of the examined studies. Specifically, the kinematic data was not transformed from the sensor location to the head center of gravity in 2 of the studies examined, which can have an effect on the resultant peak magnitudes (Allen et al. 1994; Exponent 2002). Additionally, there are limitations to the methods of other studies whose results are compared to the current findings. Specifically, there is error associated with data collected using the HIT System. Siegmund et al. (2016) validated the HIT System that it overestimated peak linear acceleration by on average by 20% and peak rotational acceleration on average by 9% (Siegmund et al. 2016). Despite these limitations, the conclusions that can be drawn from this study contribute to the field of head injury biomechanics and will aid in our understanding of head impact exposure.

5. Conclusion

A literature review was conducted to characterize head kinematics associated with everyday activities of an active person and summarized to establish an envelope of typical loading regimes of everyday activities. Kinematic data from a total of 11 studies representing 49 unique activities were summarized. All but one of the studies examined reported linear acceleration, while five of the 11 reported rotational kinematics. Linear acceleration ranged from 0.3 to 13.8 g’s while rotational acceleration ranged from 13 to 1375 rad/s2. Rotational velocity ranged from 0.7 to 12.8 rad/s and pulse duration ranged from 14.6 to 104.9 ms. Somewhere between these limits and the lower range of concussion risk are exposure limits for which more study is warranted. Identifying the lower limit of kinematic magnitudes that are of biomechanical and clinical interest is a valuable contribution to the field. This knowledge would aid in the evaluation of medical devices as well as sports and automotive safety measures by establishing what constitutes an acceptable level of exposure.

Acknowledgements

The authors would like to thank Medtronic for their support and collaboration during this project.

Funding: This study was funded by Medtronic.

Appendix A

Instrumentation details and sensor specifications for each study are listed in Table A1. There is not agreement among the sources regarding removing the gravitational acceleration signal and various authors have handled this in different ways without rigorous commentary on specific methods used. This is an important consideration when examining mild accelerations because it can add up to 1g to measured signals, which is in the range of 7-30% of the peak kinematics associated with everyday activities.

Table A1.

Instrumentation Details and Sensor Specifications

Reference Instrumentation Sensor configuration Accelerometer/
Gyroscope		 Kinematic
Range		 Sample
Frequency (Hz)
Allen et al. (1994) Helmet-mounted 3 bidirectional accelerometers - ±20g 500
Exponent (2002) Headform-mounted 3 triaxial accelerometers Entran 25G ±25g 500
Kavanagh et al. (2004) Headband-mounted 2 triaxial accelerometers CXL02LF3 ±2g 512
Ng et al. (2005) Biteplate 3 linear accelerometers Endevco 7596A ±100g 2,000
Bussone (2005) Biteplate 3 linear accelerometers and
3 angular rate sensors		 Endevco 7596A
ARS-06S		 ±100g
±200 rad/s		 2,000
Pfister et al. (2009) Biteplate 3 linear accelerometers and
3 angular rate sensors		 CXL25M3
#ENC-03J		 ±25g
±5.24 rad/s		 10,000
Bussone and Duma (2009) Biteplate 3 angular rate sensors ARS-06S ±200 rad/s 2,000
Funk et al. (2011) Biteplate 2 accelerometers and
1 angular rate sensor		 Endevco 7265-HS ±100g
±26.2 rad/s		 10,000
Bussone and Prange (2014) Headband-mounted 1 triaxial accelerometer and
1 triaxial angular rate sensor		 DTS ACC3
DTS ARS3 PRO		 ±100g
±26.2 rad/s		 10,000
Kuo et al. (2017) Mouthguard 3 linear accelerometers and
3 angular rate sensors		 -
-		 ±8g
±34.9 rad/s		 100
Carriot et al. (2014) Headband-mounted 1 triaxial accelerometer and
1 triaxial angular rate sensor		 H3LIS331DL
ITG3500A		 ±400g
±34.9 rad/s		 1,000
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Footnotes

Conflict of Interest: The authors declare that they have no conflict of interest.

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