Long-term mobility monitoring of older adults using accelerometers in a clinical environment

Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm

The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities clustered into four protocols (i.e., natural, strict, healthcare provider, and bed cycling). A video served as the reference standard, with two raters classifying the type and duration of all activities. This classification was reliable as intraclass correlations were all above 0.76 except for walking in the healthcare provider protocol, (0.29). The data of four participants were used to develop and optimize the algorithm by adjusting body-segment angles and rest-activity-threshold values based on percentage agreement (%Agr) with the reference. The validity of the algorithm was subsequently assessed using the data from the remaining six participants. %Agr of the algorithm versus the reference standard regarding lying, sitting activities, and transitions was 95%, 74%, and 80%, respectively, for all protocols except transitions with the help of a healthcare provider, which was 14-18%. For bed cycling, %Agr was 57-76%. This study demonstrated that the developed algorithm is suitable for identifying and quantifying activities common for intensive care patients. Knowledge on the (in)activity of these patients and their impact will optimize mobilization.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Validity study of a triaxial accelerometer for measuring energy expenditure in stroke inpatients of a physical medicine and rehabilitation center

PURPOSE

Establish the validity of a triaxial accelerometer (Dynaport®) for evaluating the energy expenditure of patients with stroke sequelae at a rehabilitation hospital.

METHODS

This is a cross-sectional study with 24 stroke inpatients of a rehabilitation hospital. The participants were assessed on energy expenditure by an ergospirometer system and the triaxial accelerometer simultaneously during a walk test. The data collected by both devices were compared by intraclass correlation coefficient (ICC) and Bland-Altman limits of agreement.

RESULTS

An almost perfect agreement (ICC = 0,94) in the energy expenditure measured by the accelerometer compared to the results of the ergospirometer system was found during the exercise test. The Bland-Altman analysis has shown suitable limits of agreement. Post hoc analyses with the maximum volume of oxygen and the total energy expenditure measured by the ergospirometer system evidenced significant correlation with the energy expenditure measurements by the accelerometer.

CONCLUSION

Our results evidence that the triaxial accelerometer Dynaport® and its built-in software are valid for estimating the energy expenditure of stroke sequelae during a walk exercise.

Conception and evaluation of a washable multimodal smart textile

Smart textiles can support people with specific needs and diseases, such as diabetes or heart disease. Currently there are efforts to combine continuous mobile monitoring with other health-related conditions. On this basis, algorithms could be developed that can be used to detect unusual or critical conditions. A study was to investigate whether a previously developed washable Multi-Modal Smart Textile (MMST), based on inexpensive materials, would provide valid and reliable results with regard to the vital parameters of pulse, temperature and mobility. The measurement of the vital parameters was carried out with the developed prototype MMST as well as with validated devices. All electronics including the rechargeable NiMH has been washed more than 30 times with different methods and it remained fully functional. The intraclass correlation coefficients (ICC) for pulse (temperature) measurement ranged between 0.036 and 0.232 (0.077 and 0.817) depending on the activity of the tested individuals (standing, sitting, lying down, moving). Cohen’s Kappa for the detection of the body position was 0.765. For the parameter of pulse, the results indicated an insufficient derivation for both validity and reliability. Due to flaws in the methodology applied, the validly and reliably for the parameter of temperature could not be determined. Valid and reliable results were obtained for the parameter mobility/change of position. If the MMST (after modification of the prototype) achieves reliable results, there are many advantages for people giving and receiving care on a budget price, even in threatening emergency situations.

When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults

BACKGROUND

Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall.

METHOD

In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period.

RESULTS

We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01.

CONCLUSIONS

While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recognizing activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers (10 males and 10 females, age = 43 ± 13 years) in a simulated hospital environment. A single triaxial accelerometer mounted on the trunk was used to measure body movement and recognize six activity types: lying in bed, upright posture, walking, wheelchair transport, stair ascent and stair descent. A DNN consisting of a three-layer convolutional neural network followed by a long short-term memory layer was developed for this classification problem. Additionally, features were extracted from the accelerometer data to train a support vector machine (SVM) classifier for comparison. The DNN reached 94.52% overall accuracy on the holdout dataset compared to 83.35% of the SVM classifier. In conclusion, a DNN is capable of recognizing types of physical activity in simulated hospital conditions using data captured by a single tri-axial accelerometer. The method described may be used for continuous monitoring of patient activities during hospitalization to provide additional insights into the recovery process.

Objectively Measured Physical Activity and Its Association with Functional Independence, Quality of Life and In-Hospital Course of Recovery in Elderly Patients with Proximal Femur Fractures: A Prospective Cohort Study

Background

Physical activity in elderly patients is crucial for recovery from proximal femoral fractures. Considering the limited possibilities for objective measurement, we aimed to evaluate the use of an accelerometer in this population to determine activity's association with functional independence, quality of life, and course of recovery.

Methods

52 patients undergoing operative treatment for proximal femur fractures (81.3 ± 7.5 years) were included in a prospective cohort study. 12 patients with fall but without fracture of the lower extremities (80.8 ± 9.5 years) served as control. An Axivity AX3 tracker continuously recorded signal vector magnitudes during the hospital stay. Additionally, 2 ± 1 and 8 ± 3 days (time point 1 and 2) after operation EuroQol-5D and Barthel-20 indices were evaluated.

Results

Physical activity increased in all patients with time. Multiple regression analyses revealed that a high Barthel-20 before fracture, a low age, a high body mass index, high albumin, and low C-reactive protein levels were independent predictors for high physical activity at time point 1 (p < 0.05). Physical activity correlated significantly with EuroQol-5D and Barthel-20 at time point 1 and 2 (p < 0.02). Furthermore, physical activity at time point 1 predicted EuroQol-5D, physical activity, and Barthel-20 at time point 2 (p < 0.01). A multiple regression demonstrated equal physical activity in patients with or without a hip fracture.

Conclusions

Accelerometer signals correlate with postoperative physical activity, Barthel-20 and quality of life in elderly patients. Physical activity is thereby positively influenced by a high prefall functional independence and a good nutrition status. A timely and adequate operation provided, there is no difference between patients with or without a fracture. This trial is registered with DRKS 00011934 on 10^th April 2017.

Objective monitoring of activity and Gait Velocity using wearable accelerometer following lumbar microdiscectomy to detect recurrent disc herniation

We report a case of a 39-year-old male with sciatica who underwent an L5/S1 microdiscectomy with objective physical activity measurements performed preoperatively and continually postoperatively up to 3-month using wireless accelerometer technology linked to the surgical practice; collecting distance travelled, daily step count (DSC) and Gait Velocity (GV). Preoperative, the patient was walking with a GV of 0.97 m/s and a DSC of less than 2,500. After the first month following surgery, the patient had increased mobility, with a GV of 1.58 m/s, and taking an average of over 4,500 steps per day. At day 57 postop, the patient experienced a recurrence of pain with reduction of GV, DSC and walking distance. Magnetic resonance imaging (MRI) was performed and revealed a recurrent disc herniation with further surgery on day 63, with a rapid return of function post 2^nd surgery. The use of wireless accelerometers is practical in obtaining objective physical activity measurements before and after lumbar microdiscectomy, and will assist the surgeon and rehabilitation provider to monitor outcomes, complications and assist in clinical decision making.

Using Accelerometers to Measure Physical Activity in Older Patients Admitted to Hospital

Background

Low levels of physical activity in older patients during hospitalization have been linked to loss of functional ability. Practical methods of measuring physical activity are needed to better understand this association and to measure the efficacy of interventions. The aims of this study were to evaluate the feasibility of using accelerometers to discriminate between lying, sitting, standing, and standing and moving and to determine the acceptability of the method from the patients' perspective.

Methods

A convenience sample of 24 inpatients was recruited. Participants wore accelerometers on their thigh and on their lower leg (just above the ankle) for 48 hours during their hospitalization. Postural changes and movement during the 48 hours were differentiated using derived pitch angles of the lower leg and thigh, and nongravity vector magnitude of the lower leg, respectively.

Results

On average, patients were lying for 61.2% of the recording time, sitting for 35.6%, standing but not moving 2.1%, and standing and moving 1.1%. All participants found the accelerometers acceptable to wear.

Conclusions

The methodology described in this study can be used to differentiate between lying, sitting, standing, and moving and is acceptable from a hospitalized older person's perspective.

Reliability and Construct Validity of the SENS Motion® Activity Measurement System as a Tool to Detect Sedentary Behaviour in Patients with Knee Osteoarthritis

Physical inactivity is important to address, and an objective way of measuring inactivity is by accelerometry. The objective of this study was to determine the reliability and construct validity of the SENS motion system to record physical activity and inactivity in patients with knee osteoarthritis. Participants with an age > 40 years and an average weekly pain above 0 on a numeric rating scale (0 = no pain, 10 = worst pain) were included. Participants had a total of two study visits and at each visit participants completed a standardized activity. Data from 24 participants were analysed. A mean agreement of 99% (SD 3%) for sedentary behaviour and a mean agreement of 97% (SD 9%) for active behaviour were found. The agreement for “walking” was 28% (SD 18%). Mean agreement between recordings on the two visits was 96% (SD 8%) for sedentary behaviour and 99% (SD 1%) for active behaviour. The SENS motion activity measurement system can be regarded as a reliable and valid device for measuring sedentary behaviour in patients with knee OA, whereas detection of walking is not reliable and would require further work.

Consumer physical activity tracking device ownership and use among a population-based sample of adults

Consumer physical activity tracking devices (PATs) have gained popularity to support individuals to be more active and less sedentary throughout the day. Wearable PATs provide real-time feedback of various fitness-related metrics such as tracking steps, sedentary time, and distance walked. The purpose of this study was to examine the prevalence and correlates of PAT ownership and use among a population-based sample of adults. A representative sample of adults ≥18 years (N = 1,215) from Alberta, Canada were recruited through random-digit dialing and responded to a questionnaire via computer-assisted telephone interviewing methods in summer 2016. Questionnaires assessed demographic and health behaviour variables, and items were designed to assess PAT ownership and usage. Logistic regression analysis (odds ratios) was used to assess correlates of PAT ownership and use. On average, participants (N = 1,215) were 53.9 (SD 16.7) years and 50.1% were female. Of the sample, 19.6% (n = 238) indicated they currently own and use a PAT. Participants who owned a PAT wore their device on average 23.2 days within the past month. Currently owning a PAT was significantly associated with being female (OR = 1.41, CI: 1.10 to 1.82), being <60 years of age (OR = 1.86, CI: 1.37 to 2.53), having at least some post secondary education (OR = 1.88, CI: 1.36 to 2.60), having a BMI ≥25 (OR = 1.52, CI: 1.16 to 1.99), and meeting physical activity guidelines (OR = 1.45, CI: 1.12 to 1.88). Similar correlates emerged for PAT use. Correlates significantly associated with PAT use and ownership included being female, being less than 60 years of age, having a post-secondary education, meeting physical activity guidelines, and being overweight/obese. This is the first study to examine characteristics of PAT ownership and use among Canadian adults.

Initial Experience With Real-Time Continuous Physical Activity Monitoring in Patients Undergoing Spine Surgery

STUDY DESIGN

Multicenter prospective pilot study.

OBJECTIVE

To evaluate if continuous physical activity monitoring by a personal electronic 3-dimensional accelerometer device is feasible and can provide objective data that correlates with patient-reported outcomes following spine surgery.

SUMMARY OF BACKGROUND DATA

Self-reported health-related quality-of-life (HRQOL) metrics are inherently limited by being very subjective, having a low frequency of data collection, and inconsistent follow-up.

METHODS

Inclusion criteria: adults (18+), thoracolumbar deformity or degenerative disease, and regular access to a computer with internet connection. Physical activity parameters included: number of daily steps, maximum hourly steps, and activity intensity. Patients completed the Oswestry Disability Index (ODI), the Short-Form Health Survey 36 (SF-36), and the Scoliosis Research Society-22r (SRS22) preoperatively and postoperatively at 6 weeks, 3 months, and 6 months.

RESULTS

Thirty-two patients were enrolled, 8 (25%) withdrew, 1 (3.1%) died, and 1 (3.1%) did not end up undergoing surgery resulting in 22 (68.8%) available patients. Mean preoperative and postoperative step ranges were 1278±767 to 17,800±6464 and 891±587 to 12,655±7038, respectively. Eleven patients improved in mean total daily steps at the final postoperative month with 2 having significant improvements (P<0.05). Five patients did not significantly change (P>0.05) and 6 patients had significantly lower mean total daily steps at 6 months (P<0.05). The entire cohort significantly improved in ODI, SF-36 Physical Component Summary, SRS Activity, SRS Appearance, SRS Mental, SRS Satisfaction, and SRS Total score at 6 months postoperative (P<0.05 for all). Both ODI and Physical Component Summary were significantly correlated with preoperative average total daily steps (r=-0.61, P=0.0058 and r=0.60, P=0.0114, respectively). No other HRQOL metrics were significantly correlated at baseline or at 6 months postoperative (P>0.05).

CONCLUSIONS

A prospective pilot study for continuous real-time physical activity monitoring was successfully completed. This is the first study of its kind and demonstrates a foundation to continuous physical activity monitoring following spine surgery. A larger and longer prospective study is needed to confirm long-term results and its relationship with HRQOL scores.

Validity and reliability of accelerometry in identification of lying, sitting, standing or purposeful activity in adult hospital inpatients recovering from acute or critical illness: a systematic review

Objective:

To investigate the validity and reliability of accelerometers to detect lying, sitting and standing postures or purposeful activity in hospitalized adults recovering from acute or critical illness.

Data sources:

CINAHL, MEDLINE, EMBASE, AMED, Cochrane Library, PEDro, PsycINFO and SPORTDiscuss were searched from inception to June 2017. Professional networks and reference lists of relevant articles were also searched. The main selection criteria were hospitalized adults with acute or critical illness and studies investigating the validity or reliability of accelerometers to identify body position or purposeful activity.

Review methods:

Two authors individually assessed study eligibility and independently undertook methodological quality assessment and data extraction from selected articles. A narrative synthesis of the data was undertaken.

Results:

Fifteen studies, collectively enrolling 385 hospitalized participants, were identified. Populations included stroke, the elderly, acute exacerbation of chronic respiratory disease, abdominal surgery and those recovering from critical illness. Correlations of r = 0.36 to 0.98 and levels of agreement of κ = 0.28 to 0.98 were reported for identification of lying, sitting or standing postures. Correlations of r = 0.4 to 0.8 with general activity were found, with r = 0.94 and 0.96 reported for step count. The reliability of accelerometry measurement was investigated in one study evaluating step count quantification (intraclass correlation coefficient (ICC) = 0.99, 95% confidence interval (CI) = 0.99–1.00).

Conclusion:

The validity of accelerometers to determine lying, sitting and standing postures or quantify purposeful activity within hospitalized acute or critically ill populations is variable. The reliability of accelerometry measurement within this setting remains largely unexplored.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson's disease using multiple inertial sensors

Background

Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD).

Methods

A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.

Results

Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.

Conclusions

This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.

Measuring Physical Activity and Sedentary Behavior in Youth with Type 2 Diabetes

BACKGROUND

Lifestyle interventions that encourage increasing physical activity (PA) and losing weight are critical for overweight and obese youth with comorbid conditions. Assessing PA within such lifestyle intervention efforts requires measurement tool(s) that are both accurate and appropriate for these youth. This research compares PA levels and sedentary behavior in an ethnically diverse cohort of overweight/obese youth with type 2 diabetes using both accelerometry and a questionnaire previously validated in the general youth population.

METHODS

Spearman's correlations were used to compare time spent sedentary and in different PA intensities between a questionnaire, the three-day PA recall (3DPAR), and an objective PA measure, the ActiGraph accelerometer, in 236 overweight/obese youth with diabetes.

RESULTS

Spearman correlations between 3DPAR and accelerometer results for total PA were small and not significant (rho = 0.11, p > 0.05 for males and females). Correlations for specific PA intensities (moderate/vigorous and light) were also small and not significant. Sedentary time between instruments was significant, but weakly correlated in females (rho = 0.19, p < 0.05), but not in males (rho = 0.07, p = 0.48).

CONCLUSIONS

Subjective PA measures validated in the general youth population may not be the best method for differentiating levels of movement in overweight/obese youth with type 2 diabetes, who spend most of their time in light-intensity activity and sedentary pursuits with little or no time spent in moderate/vigorous-intensity activities. Objective measures such as accelerometers that can capture the lower end of the movement scale are likely the more appropriate measures under these conditions.

Physical Activity Measured with Accelerometer and Self-Rated Disability in Lumbar Spine Surgery: A Prospective Study

STUDY DESIGN

Prospective observational study.

OBJECTIVE

Patient-based subjective ratings of symptoms and function have traditionally been used to gauge the success and extent of recovery following spine surgery. The main drawback of this type of assessment is the inherent subjectivity involved in patient scoring. We aimed to objectively measure functional outcome in patients having lumbar spine surgery using quantitative physical activity measurements derived from accelerometers.

METHODS

A prospective study of 30 patients undergoing spine surgery was conducted with subjective outcome scores (visual analog scale [VAS], Oswestry Disability Index [ODI] and Short Form 12 [SF-12]) recorded; patients were given a Fitbit accelerometer (Fitbit Inc., San Francisco, California, United States) at least 7 days in advance of surgery to record physical activity (step count, distance traveled, calories burned) per day. Following surgery, postoperative activity levels were reported at 1-, 2-, and 3-month follow-up.

RESULTS

Of the 28 compliant patients who completed the full trial period, mean steps taken per day increased 58.2% (p = 0.008) and mean distance traveled per day increased 63% (p = 0.0004) at 3-month follow-up. Significant improvements were noted for mean changes in VAS back pain, VAS leg pain, ODI, and SF-12 Physical Component Summary (PCS) scores. There was no significant correlation between the improvement in steps or distance traveled per day with improvements in VAS back or leg pain, ODI, or PCS scores at follow-up.

CONCLUSIONS

High compliance and statistically significant improvement in physical activity were demonstrated in patients who had lumbar decompression and lumbar fusion. There was no significant correlation between improvements in subjective clinical outcome scores with changes in physical activity measurements at follow-up. Limitations of the present study include its small sample size, and the validity of objective physical activity measurements should be assessed in future larger, prospective studies.

Understanding people with human activities and social interactions for human-centered computing

An ultimate goal of human-centered computing is making human beings the center of computing technologies. To make people a core component of the technology, first of all, we need to understand people and society in which people live. In this paper, we propose two important factors in order to understand people and their social interactions. Proposed human activity recognition and neighbor discovery schemes help us comprehend human activities and their social behaviors. In addition, combination of these two mechanisms provide us with an opportunity for better understanding of people in the near future. Finally, it might be worth analyzing correlation between human activities and social interactions for the group of people using our proposed schemes.

Long-Term Objective Physical Activity Measurements using a Wireless Accelerometer Following Minimally Invasive Transforaminal Interbody Fusion Surgery

We report on a case of a patient who underwent minimally invasive transforaminal lumbar interbody fusion (mi-TLIF) with objective physical activity measurements performed preoperatively and postoperatively at up to 12-months using wireless accelerometer technology. In the first postoperative month following surgery, the patient had reduced mobility, taking 2,397 steps over a distance of 1.8 km per day. However, the number of steps taken and distance travelled per day had returned to baseline levels by the second postoperative month. At one-year follow-up, the patient averaged 5,095 steps per day in the month over a distance of 3.8 km; this was a 60% improvement in both steps taken and distance travelled compared to the preoperative status. The use of wireless accelerometers is feasible in obtaining objective physical activity measurements before and after lumbar interbody fusion and may be applicable to other related spinal surgeries as well.

Physical Human Activity Recognition Using Wearable Sensors

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities

Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors

Background

Recently, much attention has been given to the use of inertial sensors for remote monitoring of individuals with limited mobility. However, the focus has been mostly on the detection of symptoms, not specific activities. The objective of the present study was to develop an automated recognition and segmentation algorithm based on inertial sensor data to identify common gross motor patterns during activity of daily living.

Method

A modified Time-Up-And-Go (TUG) task was used since it is comprised of four common daily living activities; Standing, Walking, Turning, and Sitting, all performed in a continuous fashion resulting in six different segments during the task. Sixteen healthy older adults performed two trials of a 5 and 10 meter TUG task. They were outfitted with 17 inertial motion sensors covering each body segment. Data from the 10 meter TUG were used to identify pertinent sensors on the trunk, head, hip, knee, and thigh that provided suitable data for detecting and segmenting activities associated with the TUG. Raw data from sensors were detrended to remove sensor drift, normalized, and band pass filtered with optimal frequencies to reveal kinematic peaks that corresponded to different activities. Segmentation was accomplished by identifying the time stamps of the first minimum or maximum to the right and the left of these peaks. Segmentation time stamps were compared to results from two examiners visually segmenting the activities of the TUG.

Results

We were able to detect these activities in a TUG with 100% sensitivity and specificity (n = 192) during the 10 meter TUG. The rate of success was subsequently confirmed in the 5 meter TUG (n = 192) without altering the parameters of the algorithm. When applying the segmentation algorithms to the 10 meter TUG, we were able to parse 100% of the transition points (n = 224) between different segments that were as reliable and less variable than visual segmentation performed by two independent examiners.

Conclusions

The present study lays the foundation for the development of a comprehensive algorithm to detect and segment naturalistic activities using inertial sensors, in hope of evaluating automatically motor performance within the detected tasks.

Technical and clinical view on ambulatory assessment in Parkinson's disease

With the progress of technologies of recent years, methods have become available that use wearable sensors and ambulatory systems to measure aspects of--particular axial--motor function. As Parkinson's disease (PD) can be considered a model disorder for motor impairment, a significant number of studies have already been performed with these patients using such techniques. In general, motion sensors such as accelerometers and gyroscopes are used, in combination with lightweight electronics that do not interfere with normal human motion. A fundamental advantage in comparison with usual clinical assessment is that these sensors allow a more quantitative, objective, and reliable evaluation of symptoms; they have also significant advantages compared to in-lab technologies (e.g., optoelectronic motion capture) as they allow long-term monitoring under real-life conditions. In addition, based on recent findings particularly from studies using functional imaging, we learned that non-motor symptoms, specifically cognitive aspects, may be at least indirectly assessable. It is hypothesized that ambulatory quantitative assessment strategies will allow users, clinicians, and scientists in the future to gain more quantitative, unobtrusive, and everyday relevant data out of their clinical evaluation and can also be designed as pervasive (everywhere) and intensive (anytime) tools for ambulatory assessment and even rehabilitation of motor and (partly) non-motor symptoms in PD.

A ubiquitous and low-cost solution for movement monitoring and accident detection based on sensor fusion

The low average birth rate in developed countries and the increase in life expectancy have lead society to face for the first time an ageing situation. This situation associated with the World's economic crisis (which started in 2008) forces the need of equating better and more efficient ways of providing more quality of life for the elderly. In this context, the solution presented in this work proposes to tackle the problem of monitoring the elderly in a way that is not restrictive for the life of the monitored, avoiding the need for premature nursing home admissions. To this end, the system uses the fusion of sensory data provided by a network of wireless sensors placed on the periphery of the user. Our approach was also designed with a low-cost deployment in mind, so that the target group may be as wide as possible. Regarding the detection of long-term problems, the tests conducted showed that the precision of the system in identifying and discerning body postures and body movements allows for a valid monitorization and rehabilitation of the user. Moreover, concerning the detection of accidents, while the proposed solution presented a near 100% precision at detecting normal falls, the detection of more complex falls (i.e., hampered falls) will require further study.

Modifications of skeletal muscle ryanodine receptor type 1 and exercise intolerance in heart failure

BACKGROUND

In experimental heart failure animal models, remodeling of skeletal and cardiac muscle ryanodine receptors (RyR), including phosphorylation, S-nitrosylation and oxidation, have been reported to contribute to pathologic Ca2+ release, impaired muscle function and fatigue. However, it is not known whether similar remodeling of RyR1 in skeletal muscle occurs in patients with heart failure, and if this is associated with impairment of physical activity.

METHODS

We studied 8 sedentary patients with New York Heart Association (NYHA) Class III heart failure and 7 age-matched, healthy, but sedentary controls. All heart failure patients had NYHA Class III and peak VO2, echocardiography and NT-proBNP data consistent with moderate to severe heart failure. The age-matched controls included were allowed hypertension but sub-clinical heart failure was to have been ruled out by normal peak VO2, echocardiography and NT-proBNP.

RESULTS

Exercise capacity (VO2max) differed by almost 2-fold between heart failure patients and age-matched controls. Compared with controls, skeletal muscle RyR1 in heart failure patients was excessively phosphorylated, S-nitrosylated and oxidized. Furthermore, RyR1 from heart failure patients was depleted of its stabilizing protein FK 506-binding protein 12 (FKBP12, or calstabin1).

CONCLUSIONS

For the first time we show that skeletal muscle RyR1 from human heart failure is post-translationally modified, which corroborates previous data from experimental animal studies. This indicates pathologic Ca2+ release as a potential mechanism behind skeletal muscle weakness and impaired exercise tolerance in patients with heart failure and suggests a potential target for pharmacologic intervention.

Recommendations for standardizing validation procedures assessing physical activity of older persons by monitoring body postures and movements

Physical activity is an important determinant of health and well-being in older persons and contributes to their social participation and quality of life. Hence, assessment tools are needed to study this physical activity in free-living conditions. Wearable motion sensing technology is used to assess physical activity. However, there is a lack of harmonisation of validation protocols and applied statistics, which make it hard to compare available and future studies. Therefore, the aim of this paper is to formulate recommendations for assessing the validity of sensor-based activity monitoring in older persons with focus on the measurement of body postures and movements. Validation studies of body-worn devices providing parameters on body postures and movements were identified and summarized and an extensive inter-active process between authors resulted in recommendations about: information on the assessed persons, the technical system, and the analysis of relevant parameters of physical activity, based on a standardized and semi-structured protocol. The recommended protocols can be regarded as a first attempt to standardize validity studies in the area of monitoring physical activity.

Aging society and gerontechnology: a solution for an independent living?

Recent studies report that the majority of older adults wish to live in their own homes, for as long as possible. This creates a growing interest in technologies to enable older people to remain living independently at home. The purpose of this article is to provide a narrative review of current technology appropriate for older adults' home use. The key research questions were as follow: 1- What is the evidence demonstrating that gerontechnologies are effective in enabling independent living? 2- What are devices designed specifically for frail elderly persons ? Several publications were identified about devices targeting social isolation (videophonic communication, affective orthotic devices or companion-type robots, personal emergency response systems [security]), autonomy loss (technologies for maintenance of autonomy in the activities of daily living) and cognitive disorders (cognitive orthotics, wandering management systems, telemonitoring). Very few articles dealt specifically with the frail older person. In particular, there was extremely limited evidence on use and efficacy of these devices within this population. There is a need to obtain a consensus on definition of the technologies, and also to revisit work strategies and develop innovative business models. To meet this goal, we need to create a network of technological companies, aging services organizations, end-users, academics, and government representatives to explore the real needs of the frail older population and to develop and validate new devices promoting aging at home.

Monitoring human health behaviour in one's living environment: a technological review

The electronic monitoring of human health behaviour using computer techniques has been an active research area for the past few decades. A wide array of different approaches have been investigated using various technologies including inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. It is only in recent years that research has turned towards a sensor fusion approach using several different technologies in single systems or devices. These systems allow for an increased volume of data to be collected and for activity data to be better used as measures of behaviour. This change may be due to decreasing hardware costs, smaller sensors, increased power efficiency or increases in portability. This paper is intended to act as a reference for the design of multi-sensor behaviour monitoring systems. The range of technologies that have been used in isolation for behaviour monitoring both in research and commercial devices are reviewed and discussed. Filtering, range, sensitivity, usability and other considerations of different technologies are discussed. A brief overview of commercially available activity monitors and their technology is also included.

The promise of wearable activity sensors to define patient recovery

The recent emergence of mobile health--the use of mobile telecommunication and wireless devices to improve health outcomes, services, and research--has inspired a patient-centric approach to monitor health metrics. Sensors embedded in wearable devices are utilized to acquire greater self-knowledge by tracking basic parameters such as blood pressure, heart rate, and body temperature as well as data related to exercise, diet, and psychological state. To that end, recent studies on utilizing wireless fitness activity trackers to monitor and promote functional recovery in patients suggest that collecting up-to-date performance data could help patients regain functional independence and help hospitals determine the appropriate length of stay for a patient. This manuscript examines existing functional assessment scales, discusses the use of activity tracking sensors in evaluating functional independence, and explores the growing application of wireless technology in measuring and promoting functional recovery.

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.

Methods of Measurement in epidemiology: sedentary Behaviour

BACKGROUND

Research examining sedentary behaviour as a potentially independent risk factor for chronic disease morbidity and mortality has expanded rapidly in recent years.

METHODS

We present a narrative overview of the sedentary behaviour measurement literature. Subjective and objective methods of measuring sedentary behaviour suitable for use in population-based research with children and adults are examined. The validity and reliability of each method is considered, gaps in the literature specific to each method identified and potential future directions discussed.

RESULTS

To date, subjective approaches to sedentary behaviour measurement, e.g. questionnaires, have focused predominantly on TV viewing or other screen-based behaviours. Typically, such measures demonstrate moderate reliability but slight to moderate validity. Accelerometry is increasingly being used for sedentary behaviour assessments; this approach overcomes some of the limitations of subjective methods, but detection of specific postures and postural changes by this method is somewhat limited. Instruments developed specifically for the assessment of body posture have demonstrated good reliability and validity in the limited research conducted to date. Miniaturization of monitoring devices, interoperability between measurement and communication technologies and advanced analytical approaches are potential avenues for future developments in this field.

CONCLUSIONS

High-quality measurement is essential in all elements of sedentary behaviour epidemiology, from determining associations with health outcomes to the development and evaluation of behaviour change interventions. Sedentary behaviour measurement remains relatively under-developed, although new instruments, both objective and subjective, show considerable promise and warrant further testing.

Fall algorithm development using kinematic parameters measured from simulated falls performed in a quasi-realistic environment using accelerometry

This study aims to determine the optimal temporal, angular and acceleration parameters and thresholds for an accelerometer based, chest-worn, fall detection algorithm. In total, 10 healthy male subjects performed 14 different fall types, 3 times by each. The falls were performed onto in a quasi-realistic environment consisting of mats of a minimum thickness. Optimum parameters for; t(falling): time-to-fall, θ(max): max-angle, t(θmax) : max-angle-time, t(RTStanding) : Return-to-standing-time and t(lying) : lying-time were determined using a data set consisting of a total of 420 falls.

Continuous monitoring of functional activities using wearable, wireless gyroscope and accelerometer technology

The development of functional activity monitors (FAMs) will allow rehabilitation researchers and clinicians to evaluate treatment efficacy, to monitor compliance to exercise instructions, and to provide real time feedback in the treatment of movement disorders during the performance of daily activities. The purpose of the present study was to develop and test a small sized wearable FAM system comprised of three sensors positioned on the sternum and both thighs, wireless Bluetooth transmission capability to a smartphone, and computationally efficient activity detection algorithms for the accurate detection of functional activities. Each sensor was composed of a tri-axial accelerometer and a tri-axial gyroscope. Computationally efficient activity recognition algorithms were developed, using a sliding window of 1 second, the variability of the tilt angle time series and power spectral analysis. In addition, it includes a decision tree that identifies postures such as sitting, standing and lying, walking at comfortable, slow and fast speeds, transitions between these functional activities (e.g, sit-to-stand and stand-to-sit), activity duration and step frequency. In a research lab setting the output of the FAM system, video recordings and a 3D motion analysis system were compared in 10 healthy young adults. The results show that the agreement between the FAM system and the video recordings ranged from 98.10% to 100% for all postures, transfers and walking periods. There were no significant differences in activity durations and step frequency between measurement instruments.

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.

Review of accelerometry for determining daily activity among elderly patients

OBJECTIVES

To review studies that used accelerometers to classify human movements and to appraise their potential to determine the activities of older patients in hospital settings.

DATA SOURCES

MEDLINE, CINAHL, and Web of Science electronic databases. A search constraint of articles published in English language between January 1980 and March 2010 was applied.

STUDY SELECTION

All studies that validated the use of accelerometers to classify human postural movements and mobility were included. Studies included participants from any age group. All types of accelerometers were included. Outcome measures criteria explored within the studies were comparisons of derived classifications of postural movements and mobility against those made by using observations. Based on these criteria, 54 studies were selected for detailed review from 526 initially identified studies.

DATA EXTRACTION

Data were extracted by the first author and included characteristics of study participants, accelerometers used, body positions of device attachment, study setting, duration, methods, results, and limitations of the validation studies.

DATA SYNTHESIS

The accelerometer-based monitoring technique was investigated predominantly on a small sample of healthy adult participants in a laboratory setting. Most studies applied multiple accelerometers on the sternum, wrists, thighs, and shanks of participants. Most studies collected validation data while participants performed a predefined standardized activity protocol.

CONCLUSIONS

Accelerometer devices have the potential to monitor human movements continuously to determine postural movements and mobility for the assessment of functional ability. Future studies should focus on long-term monitoring of free daily activity of a large sample of mobility-impaired or older hospitalized patients, who are at risk for functional decline. Use of a single waist-mounted triaxial accelerometer would be the most practical and useful option.

Real-time low-energy fall detection algorithm with a programmable truncated MAC

The ability to discriminate between falls and activities of daily living (ADL) has been investigated by using tri-axial accelerometer sensors, mounted on the trunk and using simulated falls performed by young healthy subjects under supervised conditions and ADL performed by elderly subjects. In this paper we propose a power-aware real-time fall detection integrated circuit (IC) that can distinguish Falls from ADL by processing the accelerations measured during 240 falls and 240 ADL. In the proposed fixed point custom DSP architecture, a threshold algorithm was implemented to analyze the effectiveness of Programmable Truncated Multiplication regarding power reduction while maintaining a high output accuracy. The presented system runs a real time implementation of the algorithm on a low power architecture that allows up to 23% power savings through its digital blocks when compared to a standard implementation, without any accuracy loss.

Detection of gait and postures using a miniaturized triaxial accelerometer-based system: accuracy in patients with mild to moderate Parkinson's disease

OBJECTIVE

To examine whether gait and postures can accurately be detected with a single small body-fixed device in patients with mild to moderate Parkinson's disease (PD).

DESIGN

Results of a triaxial accelerometer-based method were evaluated against video observation scores (criterion measure). Study 1: Subjects performed basic mobility-related activities (walking, lying, sitting, standing) in a fixed and free sequence. Study 2: Subjects were monitored while doing similar activities as in study 1 and while doing usual domestic activities.

SETTING

Study 1: Standardized set-up in a movement laboratory. Study 2: Home environment.

PARTICIPANTS

(N=37) Study 1: Patients with PD (n=32; mean age +/- SD, 67.3+/-6.6y; mean disease duration +/- SD, 6.1+/-3.4y). Study 2: Patients with PD (n=5; mean age +/- SD, 76.0+/-7.3y; mean disease duration +/- SD, 3.8+/-4.7y).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The degree of correspondence between the monitor and the video observation for the duration of each activity. Overall agreement, sensitivity, specificity, and positive predictive values were calculated.

RESULTS

Study 1: Overall agreement ranged between 69.8% and 90.8% (fixed sequence) and 57.5% and 96.9% (free sequence). Study 2: Overall agreement ranged between 60.0% and 89.2%. Lying, sitting (home), and walking were detected most accurately with mean sensitivity varying from 81.7% to 99.9%. Lower values were found for sitting (laboratory), standing, and shuffling.

CONCLUSIONS

This triaxial monitor system is a practical and valuable tool for objective, continuous evaluation of walking and postures in patients with mild to moderate PD. Detection of sitting and standing requires further fine-tuning.

Testing of a long-term fall detection system incorporated into a custom vest for the elderly

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day. The system previously achieved sensitivity of >90% and a specificity of >99%, using young healthy subjects performing falls and normal activities of daily living (ADL). In this study, over 833 hours of monitoring was performed over the course of the four weeks from the elderly subjects, during normal daily activity. In this time no actual falls were recorded, however the system registered a total of the 42 fall-alerts however only 9 were received at the care taker site. A fall detection system incorporated into a custom designed garment has been developed which will help reduce the incidence of the long-lie, when falls occur in the elderly population. However further development is required to reduce the number of false-positives and improve the transmission of messages.

The design and development of a long-term fall detection system incorporated into a custom vest for the elderly

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer, microcontroller, battery and Bluetooth module. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested on young healthy subjects performing normal activities of daily living (ADL) and falls onto crash mats, while wearing the best and sensor. Results show that falls can de distinguished from normal activities with a sensitivity >90% and a specificity of >99%, from a total data set of 264 falls and 165 normal ADL. By incorporating the fall-detection sensor into a custom designed garment it is anticipated that greater compliance when wearing a fall-detection system can be achieved and will help reduce the incidence of the long-lie, when falls occur in the elderly population. However further long-term testing using elderly subjects is required to validate the systems performance.

Activity identification using body-mounted sensors--a review of classification techniques

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

Wearable systems for monitoring mobility-related activities in older people: a systematic review

OBJECTIVE

The use of wearable motion-sensing technology offers important advantages over conventional methods for obtaining measures of physical activity and/or physical functioning in aged individuals. This review aims to identify the actual state of applying wearable systems for monitoring mobility-related activity in older populations. In this review we focus on technologies and applications, research designs, feasibility and adherence aspects, and clinical relevance of wearable motion-sensing technology.

DATA SOURCES

PubMed (MEDLINE since 1990), Ovid (BIOSIS, CINAHL), and Cochrane (Central) and reference lists of all relevant articles were searched.

REVIEW METHODS

Two authors independently reviewed randomized and non-randomized trials on people above 65 years systematically. Quality of selected articles was scored and study results were summarised and discussed.

RESULTS

Two hundred and twenty-seven abstracts were considered. After application of inclusion criteria and full text reading, 42 articles were taken into account in a full text review. Twenty of these papers evaluated walking with step counters, other papers used varying accelerometry approaches for obtaining overall activity measures (n = 16), or for monitoring changes in body postures and activity patterns (n = 17). Seven studies explicitly mentioned feasibility and/or adherence aspects. Eight studies presented outcome evaluations of interventions. Eight articles were representing descriptive research designs, three articles were using mixed descriptive and exploratory research designs, 23 articles used exploratory research-type designs, and eight articles used experimental research designs.

CONCLUSION

Although feasible methods for monitoring human mobility are available, evidence-based clinical applications of these methods in older populations are in need of further development.