Optimal sensor placement for measuring physical activity with a 3D accelerometer

Accelerometer-based activity monitors are popular for monitoring physical activity. In this study, we investigated optimal sensor placement for increasing the quality of studies that utilize accelerometer data to assess physical activity. We performed a two-staged study, focused on sensor location and type of mounting. Ten subjects walked at various walking speeds on a treadmill, performed a deskwork protocol, and walked on level ground, while simultaneously wearing five ProMove2 sensors with a snug fit on an elastic waist belt. We found that sensor location, type of activity, and their interaction-effect affected sensor output. The most lateral positions on the waist belt were the least sensitive for interference. The effect of mounting was explored, by making two subjects repeat the experimental protocol with sensors more loosely fitted to the elastic belt. The loose fit resulted in lower sensor output, except for the deskwork protocol, where output was higher. In order to increase the reliability and to reduce the variability of sensor output, researchers should place activity sensors on the most lateral position of a participant's waist belt. If the sensor hampers free movement, it may be positioned slightly more forward on the belt. Finally, sensors should be fitted tightly to the body

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Instrumenting gait with an accelerometer: A system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required

Instrumenting gait with an accelerometer: A system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required

Sensing human activity to improve sedentary lifestyle

Recent public health campaigns often communicate the alarming phrase: “Sitting is the new smoking”. Sitting is related to all-cause mortality, cardiovascular disease, type 2 diabetes, and metabolic syndrome. Sedentary behavior is generally understood as “sitting or reclining while expending ≤1.5 metabolic equivalents” and the interesting aspect of sedentary behavior is that it is a modifiable health risk. The health risk can be reduced if a person changes his or her behavior towards a healthier one; to sit less and to become more physically active. Research focusing on patterns of sedentary behavior has taken off since the rise of both wearable technologies and activity sensors. They provide opportunities for uncovering sedentary patterns within the context of daily life. As a consequence, the sedentary research field moved forward towards fine-grained, objective monitoring of sedentary behavior in free-living conditions for substantial time frames. Current wearable activity sensors are, however, not flawless in measuring sedentary behavior. It is therefore important to understand the effects of possible measurement bias, in order to deal with it in the best way. People are often unaware of their sedentary behavior, making it difficult to change the behavior. mHealth interventions can improve awareness and trigger behavior change by tailoring the intervention to the user’s needs by providing direct feedback and coaching on physical activity and sedentary behavior together with real-time information on the context. Context information can be gathered by integrating relevant data sources or by posing questions about the here-and-now. Further increase of acceptance of mHealth interventions can be achieved by tailoring to individual values, and barriers and facilitators to these values. The aim of this thesis is to determine how wearable activity sensors can be applied successfully in health interventions focused on sedentary behavior. This thesis follows an expanding scope: starting from the level of the activity sensor up to the level of public health. The first part of this thesis focuses on the measurement of sedentary behavior and its patterns by means of wearable activity sensors (Chapter 2, 3 and 4). The second part of this thesis focuses on the development and evaluation of mHealth interventions that utilize these wearable activity sensors (Chapter 5 and 6). The pattern of sedentary behavior during the day is an independent health risk. Prolonged sedentary time affects cardio-metabolic and inflammatory biomarkers, independent of the total sedentary time. Since the rise of both wearable technologies and activity sensors, there is however, no consensus among researchers on the best outcome measures for representing the sedentary pattern during the day, based on wearable activity sensors. Chapter 2 provides an overview of current pattern measures of sedentary behavior in adults, by means of a literature review. Simple measures of sedentary behavior were most often reported, like the number of bouts, the medium or median bout length. More complex pattern measures, such as the GINI index or the W50 were reported sparsely. Due to the differences among measurement devices, data analysis protocols and a lack of basic outcome parameters such as total wear-time and total sedentary time, the sedentary patterns, reported in the various studies, were difficult to compare. The simple and complex measures of sedentary time accumulation serve different goals, varying from a quick overview to in-depth analysis and prediction of behavior. The answer to which measures are most suitable to report, is therefore strongly dependent on the research question. From this overview in Chapter 2 we conclude that the reported measures were dependent on 1) the sensing method, 2) the classification method, 3) the experimental and data cleaning protocol, and 4) the applied definitions of bouts and breaks. Based on these findings, we recommend to always report total wear-time, total sedentary time, number of bouts and at least one measure describing the diversity of bout lengths in the sedentary behavior such as the W50. Additionally, we recommend to report the measurement conditions and data processing steps. One of the factors influencing the output of activity sensors mentioned above is the experimental protocol (Chapter 2). This was studied in more depth in Chapter 3 in which we focused on optimal sensor placement for measuring physical activity. Subjects walked at various speeds on a treadmill, performed a deskwork protocol, and walked on level ground, while simultaneously wearing five activity sensors with a snug fit on an elastic waist belt. We found that sensor location, type of activity, and their interaction-effect affected sensor output. The most lateral positions on the waist belt were the least sensitive for interference. Additionally, the effect of mounting was explored by repeating the experimental protocol with sensors more loosely fitted to the elastic belt. The loose fit resulted in lower sensor output, except for the deskwork protocol, where output was higher. We conclude that, in order to increase the reliability and to reduce the variability of sensor output, researchers should place activity sensors on the most lateral position of a participant‘s waist belt. If the sensor hampers free movement, it may be positioned slightly more forward on the belt. Finally, we recommend to wear sensors tightly fitted to the body. Another factor influencing the output of activity sensors mentioned above (Chapter 2) is the classification method. Currently, the most applied method to distinguish sedentary from active time is by applying a cut-point to accelerometry-based data. This means that the intensity of the measured behavior is classified as being sedentary when below this cut-point. The effect of the classification method on sedentary pattern measures was studied in detail in laboratory and free-living conditions with office workers (Chapter 4). In this study we found that the outcome measures are robust – meaning that the outcome measures do not change –, when cut-points for classifying sedentary behavior are within the boundaries of ±10-20% of the optimal cut-point. This conclusion implies that results from studies analyzing sedentary patterns based on different cut-points, can only be compared if the cut-points are within these boundaries. In Chapter 5, we combined the knowledge on sensor use, data processing steps and outcome measures with context-aware technology in an intervention for older office workers towards sitting less and breaking up sitting time. Office workers spend a high percentage of their time sitting, often in long periods of time. Research suggests that it is healthier to break these long bouts into shorter periods by being physically active. In order to promote breaking up long sedentary bouts, we developed an innovative, context-aware activity coach for older office workers. This coach provides activity suggestions, based on a physical activity prediction model, consisting of past and current physical activity (measured by a wearable activity sensor) and digital agendas. The total sedentary time in the intervention week, was not reduced compared to the baseline week. However, the pattern of the sedentary behaviour did change – the office workers reduced their total time spent in long sitting bouts (≥45 minutes). Additionally, the office workers indicated that the main added value of the intervention resided in creating awareness about their personal sedentary behaviour pattern. Finally, the participants were compliant to 53% of the suggestions; a number that could be increased by improving the timing of suggestions. We conclude that the mobile intervention (using an activity sensor, smartphone application and context information) has the potential to improve the sedentary behaviour of older office workers. The gain can especially be found in breaking up long sedentary periods by being physically active. Older office workers value that it makes them aware of their sedentary behaviour. We also found that focusing on total sedentary time as an outcome of a public health intervention, aimed at reducing sedentary behaviour, is too simplistic. Rather, one should take into account both the duration and the number of bouts when determining the effect of the intervention. We conclude this article by summarizing our design recommendations for eHealth interventions that aim to improve sedentary behaviour. In Chapter 6 we focused on a design approach to further increase acceptance of mHealth interventions – by tailoring to individual values, and barriers and facilitators to these values. In this study, we demonstrated how value-based design can contribute to the design of a product or service that addresses real needs and thus, lead to high acceptance. We described the methods and application of value-based design. We elicited values, facilitators and barriers of their reduced mobility – meaning difficulty with walking, biking, and/or activities of daily living – of older adults via in-depth interviews. These interviews resulted in a myriad of key values, such as ‘independence from family’ and ‘doing their own groceries’. Co-creation design sessions resulted in innovative mobility aids from which three designs for a wheeled walker were selected for evaluation on acceptance, again via in-depth interviews. Their acceptance was rather low. Current mobility device users were more eager to accept the designs than non-users. The value-based approach offered designers a close look into the lives of the elderly, thereby opening up a wide range of innovation possibilities that better fit the actual needs. However, mobility is related to physical capacity and not being sedentary. In-depth understanding of the values of life to be mobile, can therefore directly inspire designers focused on mobility aids. Nevertheless, this understanding can as well tap into the context and personal goals needed to tailor health interventions on sedentary behavior. In Chapter 7, the general discussion, we discuss the rapid development of sensors and analysis methods, as well as gathering rich context information by means Experience Sampling. Cut-point-based analyses make only very limited use of the wealth of data that can be measured by activity sensors and has various challenges which hinders generalization of the current body of knowledge (Chapter 2, 3 and 4). It seems to make more sense to express the measured behavior in terms of the actual behavior, such as bicycling and climbing stairs, rather than expressing physical activity in counts or metric units representing its intensity. Machine learning techniques are very capable of this with higher accuracy, and their application seems to be a logical step forwards. And when expressed as behavior, machine learning output will be easier to adopt in interventions, as total sitting time and active breaks are better understandable than for example counts – matching the real-world knowledge of users. The Experience Sampling Method (ESM) incorporated in the developed intervention (Chapter 5) provided in-depth understanding of the context of sedentary behavior – the where, when, why, with whom and experienced emotions. Future interventions should incorporate this type of context-awareness in real-time tailored coaching strategies to increase awareness and behavior change. The studies in this thesis have shown that activity sensors can provide valuable information on the pattern of sedentary behavior, and that these can be useful for health interventions. We have shown the strengths of current practice and opportunities for improvement, by applying a broad scope with a focus on measuring sedentary behavior and developing and evaluating mHealth interventions. Based on the study we conclude that: The combination of the bottom-up approach (from sensor, to data to information levels) and the top-down approach (from user values related to public health to interventions and its specific feedback and coaching strategies), contribute to diverse, strongly connected and interdependent domains of applying wearable activity sensors in health interventions focused on sedentary behavior

Advancing the measurement of sedentary behaviour : classifying posture and physical (in-)activity

Sedentary behaviour, defined by a sitting body posture with minimal-intensity physical activity, is an emergent public health topic. The time spent sedentary is associated with the incidence of non-communicable chronic diseases such as type 2 diabetes and cardiovascular disease and significantly shortens life-expectancy in a dose-response relationship. Office workers are at particular risk of developing diseases related to sedentary behaviour due to their excessive sedentary work. Even though thigh-worn posture sensors are recommended to measure sedentary behaviour, the vast majority of the evidence was collected with waist-worn physical activity sensors, and we still lack a method to measure the posture and the physical activity component of sedentary behaviour simultaneously. This thesis aims to advance the measurement of sedentary behaviour in an office context by developing new device-based methods to measure both components simultaneously, and by validating and subsequently applying the most promising method to measure the actual amount of sedentary behaviour in the daily life of office workers. The method development showed that it is possible to measure both components of sedentary behaviour with only one sensor, preferably worn on the thigh or waist. While an accelerometer is sufficient for the thigh, an inertial-measurement-unit is preferable for the waist due to a significantly improved posture classification. The method validation subsequently confirmed that waist-worn physical activity sensors, the prevailing choice to measure sedentary behaviour, measure minimal-intensity physical activity. Furthermore, the study uncovered a serious postural dependency causing a systematic overestimation of minimal-intensity physical activity while sitting compared to standing. The subsequent method application considered the posture dependency and combined a thigh-worn posture sensor with a waist-worn physical activity sensor to POPAI, the Posture and Physical Activity Index. POPAI has a sensitivity of 92.5% and a specificity of 91.9% to measure sedentary behaviour and classified 45.0% of the office workers wake-time sedentary. The posture sensor alone overestimated sedentary time by 30.3%, and the physical activity sensor alone overestimated sedentary time by 22.5%. The difference can be explained by active sitting (2.0 hours per day) and inactive standing (1.8 hours per day), both of which are much more common than previously thought. This thesis confirms the recommendation to use a thigh-worn accelerometer to measure sedentary behaviour and adds the information that such a sensor is also able to measure physical (in-)activity in sitting. Thus, there is no need to approximate sedentary behaviour with sitting, nor is there a need to approximate it with inactivity. In fact, these approximations lead to inaccurate and imprecise results substantially overestimating sedentary behaviour. Due to the predominant use of physical activity sensors to measure sedentary behaviour, recommendations to limit sedentary behaviour should address a limitation of the time spent inactive rather than the time spent sitting. If it turns out that sitting matters, one could expect a much stronger relationship between sedentary behaviour measured with a combined method such as POPAI and detrimental health effects

Multi-sensor physical activity measurement in early childhood

The purpose of this dissertation was to develop, validate, and implement multi-sensor approaches for measuring physical activity and social/contextual covariates in 2-5 year-old children via wearable-, wireless communication-, and infrared-depth camera-based technologies. In Chapter 2, a three-phased study design was used to validate a method for estimating metered distances between wearable devices using accelerometer-derived Bluetooth signals. Results showed that distances, up to 20 meters, can be predicted between a single Bluetooth beacon and receiver using a Random Forest algorithm. When multiple Bluetooth beacons and receivers were used within the same environment, a moving average filter was required to recover observations lost due to noise. Overall, simulation and validation data suggest that accelerometer-derived Bluetooth signals can be used in studies of physical activity co-participation to 1) estimate metered distances between devices using a single beacon-receiver paradigm, as well as to 2) estimate the proportion of time that devices are proximal when using multiple beacons and receivers. Chapter 3 characterized the relationship between objectively measured physical activity and dyadic spatial proximities in 2 year-olds and their parents. Data revealed that the overall proportions of time that children and their parents spent in total physical activity were positively associated, and time series data revealed that this relationship remained consistent when analyzed hour-to-hour. Time spent engaged in sedentary behavior was also positively associated between children and parents; however, there was no association between child and parent moderate-vigorous physical activity volumes. Dyadic proximity results showed that girls spent more time in joint physical activity with their mothers than boys. Furthermore, children who engaged in >60 minutes of daily moderate-vigorous physical activity spent an additional 30 minutes in joint total physical activity with their mothers each day, on average, when compared to children who engaged in 60 minutes of daily moderate-vigorous physical activity participated in joint physical activity with their mothers across wider relative distances, on average, than did boys who engaged in physical activity at closer relative distances to their mothers. In Chapter 4, an original computer vision algorithm was applied to infrared-depth camera data for the purpose of converting three-dimensional videos into triaxial physical activity signals in young children. Physical activity data were collected in 2-5 year-old children during 20-minute semi-structured, indoor child-parent dyadic play sessions. Play session video data were converted into triaxial physical activity signals using a multi-phased computer vision algorithm for each child. Computer vision-derived triaxial physical activity cut points for 2-5 year-olds were calibrated against a direct observation reference system using a machine learning algorithm. Results revealed that triaxial activity signals, as measured by a dual-sensor camera, can be used to estimate both physical activity intensities and volumes in young children without the use of wearable technology. Collectively, these studies show that multi-sensor approaches to physical activity measurement are a valid means by which to measure physical activity and social/contextual covariates in young children using either wearable sensors or computer vision

A new multisensor software architecture for movement detection: Preliminary study with people with cerebral palsy

A five-layered software architecture translating movements into mouse clicks has been developed and tested on an Arduino platform with two different sensors: accelerometer and flex sensor. The archi-tecture comprises low-pass and derivative filters, an unsupervised classifier that adapts continuously to the strength of the user's movements and a finite state machine which sets up a timer to prevent in-voluntary movements from triggering false positives. Four people without disabilities and four people with cerebral palsy (CP) took part in the experi-ments. People without disabilities obtained an average of 100% and 99.3% in precision and true positive rate (TPR) respectively and there were no statistically significant differences among type of sensors and placement. In the same experiment, people with disabilities obtained 97.9% and 100% in precision and TPR respectively. However, these results worsened when subjects used the system to access a commu-nication board, 89.6% and 94.8% respectively. With their usual method of access-an adapted switch- they obtained a precision and TPR of 86.7% and 97.8% respectively. For 3-outof- 4 participants with disabilities our system detected the movement faster than the switch. For subjects with CP, the accelerometer was the easiest to use because it is more sensitive to gross motor motion than the flex sensor which requires more complex movements. A final survey showed that 3-out-of-4 participants with disabilities would prefer to use this new technology instead of their tra-ditional method of access