Wearable motion sensors with built-in accelerometers have been deployed for gait assessment. This study aims at exploring gait patterns between younger and older adults using a motion-sensing system and exploring sensor technology acceptance among participants. The motion-sensing system was formed by a smart bracelet, an Android application, and a website based on Microsoft Azure. The study employed quasi-experimental, nonexperimental, and qualitative design. A total of 28 younger and 28 older adults were recruited. The gait assessment result indicated that the root mean square (RMS) acceleration increased significantly as the walking pace increased based on the right ankle sensor. Older participants usually presented a lower magnitude of acceleration patterns in the anteroposterior and mediolateral direction compared with the younger participants, while the stride regularity and variability were not significantly different between younger and older participants. User evaluation indicated that the user experience of the motion-sensing system could be further enhanced by providing feedback on the smart bracelet display, generating an analysis report on the gait visualization website, and involving family members in data sharing for older adults. Study findings demonstrated that it is feasible to use portable motion-sensing methods to measure gait characteristics among Chinese adults. Suggestions proposed through user evaluation could be of value to improve the user experience of the motion-sensing system.
Wearable devices for older adults with the function of health management have become popular in recent years. Smart bracelets, such as the Jawbone UP, the Fitbit Flex, and the Garmin Vivofit, allow users to track their activities, nutrition, sleeping patterns, [
This study employed a motion sensor on a smart bracelet to conduct gait assessment of younger and older adults. The gait data were collected with an Android application, and the results were then visualized on a website. This study engaged younger and older adults in the evaluation process to acquire knowledge about their perceptions of the motion-sensing system. The motion-sensing system that will be discussed in this work is promising for gait assessment in home settings and for engaging the cooperation of different parties such as older adults and their family members or care givers.
More specifically, we explored the following two research objectives: (1) to test feasibility of measuring gait patterns among younger and older Chinese adults using portable motion-sensing methods; (2) to dig user requirements to improve the user experience of the motion-sensing system. The system described in this study has the potential to be expanded to a telemedicine service. Suggestions proposed through user evaluation could be of value to improve the user experience of the motion-sensing system.
Gait is an important index for observing the mobility of older adults. Gait parameters, such as speed, cadence, stride length, and gait variability, are useful for the detection of frailty or fall risk of older adults [
In recent years, many wearable devices with built-in accelerometers have been used as an innovative way to assess gait. The sensors are placed on several locations, such as the pelvis [
Although older adults may be assisted by motion sensors in their daily life, they may encounter difficulty in using technology. Many studies have explored users’ acceptance of health-related information and communication technology (ICT) products. For example, Vaziri et al. [
Regarding factors influencing technology acceptance, the well-known technology acceptance model stresses the importance of perceived usefulness and perceived ease of use when designing information technology for older adults [
Researchers state that “one way to facilitate older adults’ adoption is through visualizations that incorporate data from smarthome sensors into relevant and insightful resources” [
Twenty-eight students (14 females and 14 males) were recruited from a university, whereas twenty-eight older participants (18 males and 10 females) were recruited from a community in Jiangbei District, Chongqing, China. The inclusion criterion was that the older participants were aged over 55 years, living in the community, and able to walk independently without walking aids. People were excluded if they had any musculoskeletal, neurological disease, or painful conditions. All participants were asked to give a written informed consent prior to participation. The study ethics were approved by Tsinghua University. Participants’ characteristics were collected via a background questionnaire, as presented in Table
Characteristics of participants in this study (
Variables | Younger ( |
Older ( |
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Age, mean (SD), y | 24.6 (2.7) | 66.1 (5.0) | <0.001 |
Gender, |
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Male | 14 | 18 | 0.280 |
Female | 14 | 10 | |
Height, mean (SD), cm | 169.7 (8.2) | 160.6 (7.1) | <0.001 |
Weight, mean (SD), kg | 61.8 (10.4) | 61.4 (7.3) | 0.859 |
BMIa, mean (SD) | 21.4 (2.6) | 23.9 (3.1) | 0.002 |
Education, |
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Primary | 0 | 16 | <0.001 |
Junior | 0 | 11 | |
Senior | 0 | 1 | |
Undergraduate | 5 | 0 | |
Graduate | 23 | 0 | |
Smartphone owner, |
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Yes | 28 | 3 | <0.001 |
No | 0 | 25 | |
Smart bracelet experienceb, |
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Yes | 16 | 0 | <0.001 |
No | 12 | 28 | |
Fall history in the last yearc, |
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Yes | 6 | 8 | 0.537 |
No | 22 | 20 | |
Self-reported health statusd, mean (SD) | 6.3 (0.9) | 4.9 (1.2) | 0.005 |
Self-reported walking abilityd, mean (SD) | 6.3 (0.7) | 5.6 (1.3) | 0.018 |
Figure
System architecture of the motion-sensing system.
The manner of wearing the smart bracelet.
We initiated the motion-sensing system with the following steps. First, the smart bracelet and the smartphone were connected through Bluetooth. The sensor data were collected by an Android application that recorded the acceleration and Euler angle in real time when the participant was walking. The application was made using Unity. The motion-sensing system (
An example of motion-sensor data of the ankle when the participant was walking back and forth. The collected data were visualized on a cloud-based website. The website interface displayed the acceleration and Euler angle of the three axes. (a) The acceleration pattern and (b) the Euler angle pattern. The
Participants first signed consent forms and filled in a questionnaire regarding their age, gender, education, occupation, experience of using smartphones and smart bracelets, self-reported health status, and walking ability on a seven-point Likert scale. Their height and weight were measured as well.
Then, they wore bracelets that had been calibrated beforehand on their wrists and ankles. The participants were asked to walk ten times along a 14 m corridor at three self-selected paces: slow, normal, and fast. The initial and final 2 m were used for acceleration and deceleration. Thus, gait assessment was performed over 10 m. Two tapes were fixed on the start line and the finish line as markers. The instructions were given to each participant in a standard form, as follows:
After the walking session, participants were asked about which parts of the body they most liked to wear the sensor on: the wrist, ankle, back, sole, or other parts of the body, on a seven-point Likert scale, with 1 indicating
During the gait test, participants wore motion sensors on their wrists and ankles. We found that some participants did not have the habit of swinging their arms when walking, and consequently, there were no or few waveforms in the acceleration patterns. This caused the MATLAB program to fail to detect the peak of the waveform. On the other hand, the ankle data showed periodic waveforms as the foot struck the floor. Therefore, ankle gait data were used for stride analysis. Specifically, we used right ankle data to maintain consistency. As the vertical acceleration signal of the ankle data showed more significant periodicity (see Acceleration
Acceleration patterns of ankle data of a younger participant: (a) walking speed = 1.2 m/s and an older participant: (b) walking speed = 0.69 m/s, walking at a normal pace. The scale of acceleration is in units of gravity (
The following gait parameters were calculated:
Here,
The autocorrelation coefficient was calculated using the
The positive peak of the acceleration was firstly detected by the
Regarding demographics, normality was assessed using the Kolmogorov–Smirnov test. For the measures that were distributed normally, independent
The motion sensor data were preprocessed using the MATLAB toolbox. As data were not constantly sampled, we adjusted the sampling rate of the acceleration signal to 100 Hz using the interpolation. A low-pass Butterworth filter with a cutoff frequency of 10 Hz was applied to filter the data. The gait parameters were then derived by a self-designed MATLAB program.
The statistical analysis of gait parameters was conducted in an R environment. The mean and 95% confidence intervals (CIs) were calculated for the averaged gait parameters for each walking pace of younger and older participants. A two-way mixed analysis of variance (ANOVA) was conducted to investigate the effect of age group and walking pace (slow, normal, and fast) on gait parameters (speed, stride frequency, average stride length, stride regularity, stride time variability, and RMS acceleration). The within-subject variable was the walking pace, and the between-subject variable was the age. After the ANOVA, if the walking pace or the interaction effects were significant,
For importance rating of acceptability aspects of the motion-sensing system, Mann–Whitney
The older group had significantly higher body mass index (BMI) value, lower height, education level, self-reported health status and walking ability, less number of smartphone owners, and smart bracelet experience than the younger group. Height and BMI were distributed normally; therefore, the independent
Descriptive statistics of the gait parameters are presented in Table
Mean (95% CI) of gait parameters of younger and older adults under different walking paces (
Gait parameters | Younger ( |
Older ( |
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Slow | 1.01 (0.93–1.09) | 0.97 (0.89–1.05) |
Normal | 1.34 (1.26–1.41) | 1.19 (1.12–1.27) |
Fast | 1.79 (1.70–1.89) | 1.46 (1.36–1.55) |
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Slow | 0.81 (0.78–0.85) | 0.84 (0.81–0.88) |
Normal | 0.97 (0.94–1.00) | 0.93 (0.91–0.96) |
Fast | 1.15 (1.11–1.20) | 1.02 (0.97–1.06) |
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Slow | 1.24 (1.17–1.31) | 1.14 (1.08–1.21) |
Normal | 1.40 (1.33–1.47) | 1.29 (1.22–1.35) |
Fast | 1.57 (1.49–1.65) | 1.44 (1.37–1.52) |
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Slow | 0.79 (0.75–0.83) | 0.81 (0.77–0.85) |
Normal | 0.81 (0.78–0.83) | 0.82 (0.80–0.85) |
Fast | 0.79 (0.76–0.82) | 0.80 (0.77–0.83) |
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Slow | 4.92 (4.10–5.74) | 5.45 (4.63–6.27) |
Normal | 4.31 (3.49–5.13) | 4.36 (3.54–5.18) |
Fast | 4.54 (3.90–5.20) | 5.00 (4.36–5.65) |
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Slow | 0.36 (0.30–0.41) | 0.20 (0.15–0.26) |
Normal | 0.46 (0.39–0.53) | 0.24 (0.17–0.31) |
Fast | 1.57 (1.49–1.65) | 1.44 (1.37–1.52) |
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Slow | 0.24 (0.21–0.26) | 0.22 (0.20–0.24) |
Normal | 0.36 (0.31–0.40) | 0.26 (0.21–0.31) |
Fast | 0.42 (0.37–0.50) | 0.31 (0.26–0.36) |
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Slow | 0.30 (0.24–0.36) | 0.41 (0.36–0.47) |
Normal | 0.44 (0.36–0.51) | 0.51 (0.44–0.58) |
Fast | 0.58 (0.50–0.66) | 0.61 (0.53–0.68) |
Interaction effects and main effects on gait parameters.
Gait parameters | Walking pace | Age | Walking pace × age |
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Gait speed |
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Stride frequency |
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Stride length |
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Stride regularity |
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Stride time variability |
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AP RMS |
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ML RMS |
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VT RMS |
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Multiple comparisons of the walking pace in terms of the gait speed, stride frequency, and ML RMS.
Walking pace | Gait speed | Stride frequency | ML RMS | |||
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Younger | Older | Younger | Older | Younger | Older | |
Slow vs. normal | 0.33 |
0.23 |
0.15 |
0.09 |
0.12 |
0.04 |
Normal vs. fast | 0.46 |
0.26 |
1.84 |
0.08 |
0.07 | 0.05 |
Slow vs. fast | 0.79 |
0.49 |
3.38 |
0.17 |
0.19 |
0.09 |
Pairwise comparisons of the walking pace in terms of the stride length, AP RMS, and VT RMS.
Walking pace | Stride length | AP RMS | VT RMS |
---|---|---|---|
Slow vs. normal | 0.15 |
0.07 |
0.12 |
Normal vs. fast | 0.16 |
1.15 |
0.12 |
Slow vs. fast | 0.31 |
1.23 |
0.24 |
The gait assessment showed that walking pace had a significant influence on the acceleration patterns collected by the motion sensor. The RMS acceleration increased significantly as the walking pace increased. Older participants usually presented a lower magnitude of acceleration patterns in the anteroposterior and mediolateral direction compared with the younger participants, while the stride regularity and variability were not significantly different. The AP RMS was significantly correlated with the walking speed (Pearson
To understand the attitudes of the participants towards the motion-sensing system, they were asked to rate the importance of the acceptability aspects of the motion-sensing system. For those older adults who had difficulty in reading, the items of the questionnaire were read aloud. The researchers explained the meanings of the items to the older adults if they did not understand the questions.
As presented in Table
Mean (SD) of importance rating of factors in accepting the motion-sensing system (
Items on acceptability of the motion-sensing system | Younger ( |
Older ( |
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The product will not harm the body | 6.8 (0.5) | 6.2 (1.3) | 0.010 |
Accuracy of measurement results | 6.7 (0.5) | 6.2 (1.1) | 0.049 |
An expert can interpret the data for me | 5.5 (1.6) | 6.0 (1.1) | 0.196 |
Familiarize myself with gait information | 5.8 (1.2) | 5.6 (1.7) | 0.787 |
Changes in the gait pattern could be observed | 5.6 (1.3) | 5.5 (1.5) | 0.705 |
My ability to learn how to use the bracelet | 4.5 (1.8) | 5.4 (1.7) | 0.044 |
The cost of the bracelet | 5.5 (1.4) | 4.7 (1.7) | 0.029 |
Good appearance of the bracelet | 4.9 (1.6) | 4.6 (1.9) | 0.603 |
Family support | 3.6 (1.6) | 4.6 (2.1) | 0.029 |
I feel fashionable when wearing the bracelet | 4.0 (1.8) | 4.3 (2.0) | 0.387 |
Remaining anonymous when using the product | 4.9 (1.7) | 4.0 (2.0) | 0.087 |
I can see information about other people | 3.5 (1.4) | 4.0 (2.0) | 0.239 |
I can compete with others | 3.6 (1.5) | 3.9 (2.0) | 0.752 |
Inconspicuousness of the bracelet | 5.2 (1.5) | 3.8 (2.0) | 0.007 |
Protection of the privacy of personal data | 5.3 (1.7) | 3.6 (2.0) | 0.003 |
Compared with younger participants, older participants regarded “ability to learn how to use the bracelet” and “family support” more important. Older adults regarded “the product will not harm the body,” “accuracy of measurement results,” “the cost of the bracelet,” “inconspicuousness of the bracelet,” and “protection of the privacy of personal data” less important than younger participants.
Regarding position to wear the motion sensor, both younger and older adults preferred to wear the motion sensor on the wrist rather than on the ankle, back, or sole (Figure
Participant ratings of the preferred positions in which to wear the motion sensor (1 =
In terms of the participants’ expectations of the motion sensor, the most reported functions included the step count (22 participants), heart rate (19), blood pressure (18), disease detection and reminders (15), and gait balance (10), as presented in Figure
Most frequently mentioned expectations of the motion sensors.
Regarding the appearance of the motion sensors, seven younger participants complained about the rectangular shape and the broad wrist parts; they would prefer a smoother appearance because it would make them feel “smart” in terms of appearance. On the other hand, three older participants mentioned they would accept the bracelet more readily if it looked like a traditional wristwatch. Two older participants suggested that it would be better if the smart bracelet was equipped with a screen.
Regarding persons authorized to view the data, the participants were interviewed about persons authorized to view their gait data (themselves, family members, doctor, or nurse). The mentioned frequency would be recorded. As presented in Figure
Persons authorized to view the data.
Regarding privacy issues, thirteen younger participants and five older participants expressed their concerns about data privacy. Some younger participants felt unwilling to publicize their data with their personal information attached (e.g., facial features) but they were willing to publicize their data anonymously for use in scientific research. Five older participants were worried that the data could be utilized illegally by other people. However, some participants did not view gait information as a private form of data. Four older participants mentioned that they hoped someone could view the data and help interpret the results.
Regarding the data display form, most younger and older participants thought the current form of visualization of the gait on a line chart was difficult to understand. Eight older and eight younger participants mentioned that they preferred to see graphs combined with written reports to obtain information about the results. They perceived the feedback of the gait assessment system as more like a professional report with a graph, conclusion, and doctor’s advice.
At the end of the experiment, participants were interviewed about their intention to use the system. Participants had mixed opinions towards using the system. Six older and three younger participants thought they would use the motion-sensing system in daily life. The reasons given by the participants were “
Eleven older and seven younger participants conveyed that they would not use the system. The main reasons given were as follows: “
Eleven older participants and fifteen younger participants said that their intention to use the system would depend on the situation. The reasons given were as follows: “
Based on the user evaluations, we identified the following design recommendations to improve the user experience of the system: Older adults were interested in having more biometric information such as blood pressure, blood glucose, and cholesterol as well as gait information. Their information needs are strongly correlated with their own health statuses. Regarding the appearance of the bracelet, a soft shape, such as that of a traditional wristwatch, would be more favourable for older adults. Real-time feedback should be displayed on the bracelet interface. Preferably, the smart bracelet should be equipped with a screen. The interface of the data display should be improved to enable users to understand the results better. For example, a gait analysis report is required to explain the results with graphs, conclusions, and medical advice. To reduce users’ privacy concerns, identifiable personal information such as facial features should not be shown. Older adults in this study were willing to share data with their family members, therefore, involving family members may facilitate the process of using the system.
This study suggested that it was feasible to conduct gait assessment using a portable motion sensor on a smart bracelet. We could place it on the ankle to measure gait parameters. The visualization website could provide health-related information about gait performance. For example, stride frequency indicates the gait cycle; stride time variability is commonly considered as a fall-risk predictor [
Gonzálezlandero et al. [
There are several suggestions for improving the user experience of the motion-sensing system. First, the appearance of the smart bracelet could be improved to increase user acceptance. Older adults tend to relate the smart bracelet to a traditional wristwatch. They wanted to view real-time feedback on the display. Second, both younger and older adults found the visualization of gait information difficult to understand because there was no summary to provide information about the results. The interface of the visualization website should be improved to enable users to understand the results better. For example, a report on the gait is necessary to explain the results with graphs, conclusions, and medical advice. In addition, we found that most of the older adults were open to the idea of sharing their gait information with their family members rather than doctors or nurses. Family support is especially important for older Chinese users, because Chinese people have interdependent self-construal and tend to rely on each other [
In conclusion, it is feasible to use portable motion sensors on smart bracelets and smartphones to measure gait characteristics. The user experience of the motion-sensing system could be further enhanced by providing feedback on the display of the smart bracelet, generating an analysis report on the gait visualization website and involving family members in data sharing for older adults.
The derived data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by the National Natural Science Foundation of China (grant number 71661167006). The authors thank Prof. Jia Zhou for her help in conducting the experiment.