Wearable Motion Analysis System for Thoracic Spine Mobility With Inertial Sensors.

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states.

A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement.

BACKGROUND: To complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. RESEARCH QUESTIONS: To evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. METHODS: 29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. RESULTS: The step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. SIGNIFICANCE: Robust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators.

COVID-19 Stay-at-home mandates impacts daily ambulatory bout intensity and duration in elementary school-aged children: A wearable sensor based analysis.

INTRODUCTION: SARS COVID-19 pandemic resulted in major changes to how daily life was conducted. Health officials instituted policies to decelerate the spread of the virus, resulting in changes in physical activity patterns of school-aged children. The aim of this study was to utilize a wearable activity monitor to assess ambulatory activity in elementary-school aged children in their home environment during a COVID-19 Stay-at-Home mandate. METHODS: This institutional review board approved research study was performed between April 3rd - May 1st of 2020 during which health officials issued several stay-at-home (shelter-in-place) orders. Participant recruitment was conducted using a convenience sample of 38 typically developing children. Participants wore a StepWatch Activity Monitor for one week and data were downloaded and analyzed to assess global ambulatory activity measures along with ambulatory bout intensity/duration. For comparison purposes, SAM data collected before the pandemic, of a group of 27 age-matched children from the same region of the United States, was included. Statistical analyses were performed comparing SAM variables between children abiding by a stay-at-home mandate (Stay-at-Home) versus the Historical cohort (alpha=0.05). RESULTS: Stay-at-Home cohort took on average 3737 fewer daily total steps compared to the Historical cohort (p<0.001). Daily Total Ambulatory Time (TAT), across all days was significantly lower in the Stay-at-Home cohort compared to the Historical cohort (mean difference: 81.9 minutes, p=0.001). The Stay-at-Home cohort spent a significantly higher percentage of TAT in Easy intensity ambulatory activity (mean difference: 2%, p<0.001) and therefore a significantly lower percentage of TAT in Moderate+ intensity (mean difference: 2%, p<0.001). CONCLUSIONS: The stay-at-home mandates resulted in lower PA levels in elementary school-aged children, beyond global measures to also bout intensity/duration. It appears that in-person school is a major contributor to achieving higher levels of PA and our study provides additional data for policymakers to consider for future decisions.

Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep.

Objective.This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology.Approach.HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.Main results.HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) - Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.Significance.Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.

Predicting the onset of psychotic experiences in daily life with the use of ambulatory sensor data - A proof-of-concept study.

INTRODUCTION: Predictive models of psychotic symptoms could improve ecological momentary interventions by dynamically providing help when it is needed. Wearable sensors measuring autonomic arousal constitute a feasible base for predictive models since they passively collect physiological data linked to the onset of psychotic experiences. To explore this potential, we investigated whether changes in autonomic arousal predict the onset of hallucination spectrum experiences (HSE) and paranoia in individuals with an increased likelihood of experiencing psychotic symptoms. METHOD: For 24 h of ambulatory assessment, 62 participants wore electrodermal activity and heart rate sensors and were provided with an Android smartphone to answer questions about their HSE-, and paranoia-levels every 20 min. We calculated random forests to detect the onset of HSEs and paranoia. The generalizability of our models was tested using leave-one-assessment-out and leave-one-person-out cross-validation. RESULTS: Leave-one-assessment-out models that relied on physiological data and participant ID yielded balanced accuracy scores of 80 % for HSE and 66 % for paranoia. Adding baseline information about lifetime experiences of psychotic symptoms increased balanced accuracy to 82 % (HSE) and 70 % (paranoia). Leave-one-person-out models yielded lower balanced accuracy scores (51 % to 58 %). DISCUSSION: Using passively collectible variables to predict the onset of psychotic experiences is possible and prediction models improve with additional information about lifetime experiences of psychotic symptoms. Generalizing to new individuals showed poor performance, so including personal data from a recipient may be necessary for symptom prediction. Completely individualized prediction models built solely with the data of the person to be predicted might increase accuracy further.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring.

OBJECTIVES: Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question. METHODS: Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events). RESULTS: Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures. CONCLUSION: Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

Early discontinuation of ambulatory vEEG among individuals with intellectual disabilities: A retrospective chart review.

OBJECTIVE: This retrospective chart review aims to quantify the rate of patients with intellectual disability (ID) accessing an Australian ambulatory EEG service, and understand the clinical implications of discontinuing studies prematurely. METHODS: Electronic records of referrals, patient monitoring notes, and EEG reports were accessed retrospectively. Each referral was assessed to determine whether the patient had an ID. For each study where patients were discharged prematurely, the outcomes of their EEG report were assessed and compared between the ID and non-ID groups. Exploratory analysis was performed assessing the effects of age, the percentage of the requested monitoring undertaken, and outcome rates as a function of monitoring duration. RESULTS: There were significantly more patients in the ID group with early disconnection than the non-ID group (Chi squared test, p = 0.000). There was no significant difference in the rates of clinical outcomes between the ID and non-ID groups amongst patients who disconnected early. CONCLUSIONS: Although rates of early disconnection are higher in those with ID, study outcomes are largely similar between patients with and without ID in this retrospective analysis of an ambulatory EEG service. SIGNIFICANCE: Ambulatory EEG is a viable modality of EEG monitoring for patients with ID.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

OBJECTIVE: Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. METHODS: We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. RESULTS: Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. CONCLUSIONS: Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Validation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease.

BACKGROUND: Consumer and research activity monitors have become popular because of their ability to quantify energy expenditure (EE) in free-living conditions. However, the accuracy of activity trackers in determining EE in people with Huntington's Disease (HD) is unknown. RESEARCH QUESTION: Can the ActiGraph wGT3X-B or the Fitbit Charge 4 accurately measure energy expenditure during physical activity, in people with HD compared to Indirect Calorimetry (IC) (Medisoft Ergo Card)? METHODS: We conducted a cross-sectional, observational study with fourteen participants with mild-moderate HD (mean age 55.7 ± 11.4 years). All participants wore an ActiGraph and Fitbit during an incremental test, running on a treadmill at 3.2 km/h and 5.2 km/h for three minutes at each speed. We analysed and compared the accuracy of EE estimates obtained by Fitbit and ActiGraph against the EE estimates obtained by a metabolic cart, using with Intra-class correlation (ICC), Bland-Altman analysis and correlation tests. RESULTS: A significant correlation and a moderate reliability was found between ActiGraph and IC for the incremental test (r = 0.667)(ICC=0.633). There was a significant correlation between Fitbit and IC during the incremental test (r = 0.701), but the reliability was poor at all tested speeds in the treadmill walk. Fitbit significantly overestimated EE, and ActiGraph underestimated EE compared to IC, but ActiGraph estimates were more accurate than Fitbit in all tests. SIGNIFICANCE: Compared to IC, Fitbit Charge 4 and ActiGraph wGT3X-BT have reduced accuracy in estimating EE at slower walking speeds. These findings highlight the need for population-specific algorithms and validation of activity trackers.

Ambulatory EEG-video.

Hospital based EEG recordings have been the norm to assist in the diagnosis and management of patients with unclassified events and known drug resistant epilepsy. Ambulatory EEG (AEEG) is a tool that comes to serve the needs for a portable testing that can be done at home, often with higher accessibility compared to an epilepsy monitoring unit and with lower cost. The current technology provides good quality EEG tracing and can be done with video when needed. In this review we discuss how AEEG should be performed and the preferred indications in which this test may be of utmost help. The advent of ultra-long ambulatory recording may be the future for selected patients as this technology evolves.

Diagnostic Yield of Ambulatory Reflux Monitoring Systems for Evaluation of Chronic Laryngeal Symptoms.

INTRODUCTION: Among patients with chronic laryngeal symptoms, ambulatory reflux monitoring off acid suppression is recommended to evaluate for laryngopharyngeal reflux (LPR). However, reflux monitoring systems are diverse in configuration and monitoring capabilities, which present a challenge in creating a diagnostic reference standard in these patients. This study aimed to compare diagnostic yield and performance between reflux monitoring systems in patients with chronic laryngeal symptoms. METHODS: This multicenter, international study of adult patients referred for evaluation of LPR over a 5-year period (March 2018-May 2023) assessed and compared diagnostic yield of pathologic gastroesophageal reflux (GER+) on ambulatory reflux monitoring off acid suppression. RESULTS: Of 813 patients, 296 (36%) underwent prolonged wireless pH, 532 (65%) underwent 24-hour pH-impedance monitoring, and 15 (2%) underwent both tests. Overall diagnostic yield for GER+ was 36% and greater for prolonged wireless pH compared with that for 24-hour pH-impedance monitoring (50% vs 27%; P < 0.01). Among 15 patients who underwent both prolonged wireless pH and 24-h pH-impedance monitoring, concordance between systems for GER+ was 40%. The most common source of discordance was strong evidence of GER+ across multiple days on prolonged wireless pH compared with no evidence of GER+ on pH-impedance. DISCUSSION: In this multicenter international study of patients with chronic laryngeal symptoms referred for LPR evaluation, diagnostic yield of ambulatory reflux monitoring off acid suppression was 36% and rose to 50% when using wireless pH monitoring. In patients referred for chronic laryngeal symptoms, 24-hour pH-impedance monitoring may risk a low negative predictive value in patients with unproven GER+ disease.

Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

Registro español de marcapasos. XX informe oficial de la Asociación del Ritmo Cardiaco de la Sociedad Española de Cardiología (2022) / Spanish pacemaker registry. 20th official report of the Heart Rhythm Association of the Spanish Society of Cardiology (2022)

Introducción y objetivos: En este informe se comunica la actividad de estimulación cardiaca en 2022: número total de implantes, adherencia a la monitorización a distancia, factores demográficos y clínicos y características del material implantado. Métodos: Las fuentes de información son la plataforma CardioDispositivos, la tarjeta europea del paciente portador de marcapasos y los datos facilitados por los fabricantes. Resultados: Las tasas de marcapasos convencionales y resincronizadores de baja energía fueron de 866 y 34 unidades/millón respectivamente. Se implantaron 815 marcapasos sin cables. Se registraron 16.426 procedimientos de 82 hospitales (9.407 a través de CardioDispositivos), lo que supone un 40% de la actividad. La media de edad fue 78,6 años, con predominio de varones (60,3%). El bloqueo auriculoventricular fue el trastorno más frecuente y el 14,5% de los pacientes estaban en fibrilación auricular. Predomina el modo de estimulación DDD/R (55,6%) y la edad influye en el modo de estimulación, de forma que más de un tercio de los pacientes mayores de 80 años en ritmo sinusal recibieron estimulación monocameral en ventrículo. Se incluyeron en monitorización a distancia el 35% de los marcapasos y el 55% de los resincronizadores de baja energía. Conclusiones: Aumentan en un 5,6% el número de marcapasos convencionales, un 16% los resincronizadores de baja energía y un 25% los marcapasos sin cables. Se estabiliza la adherencia a la monitorización a distancia. Aumenta en un 11% el número de procedimientos incluidos en CardioDispositivos, aunque disminuye el volumen de muestra. El uso extensivo de la plataforma es lo que permitirá en años venideros contar con un registro de calidad.(AU)

Introduction and objectives: This article reports the cardiac pacing activity performed in 2022, including the total number of implants, adherence to remote monitoring, demographic and clinical factors, and the characteristics of the implanted devices. Methods: The information sources were the CardioDispositivos online platform, the European pacemaker patient identification card, and data provided by the manufacturers. Results: The rates of conventional pacemakers and low-energy resynchronizers were 866 and 34 units per million population, respectively. A total of 815 leadless pacemakers were implanted. In all, 16426 procedures performed in 82 hospitals were reported (9407 through CardioDispositivos), representing 40% of the activity. The mean age was 78.6 years, with a predominance of men (60.3%). The most frequent disorder was atrioventricular block, and 14.5% of the patients had atrial fibrillation. There was a predominance of the DDD/R pacing mode (55.6%), and pacing mode was influenced by age, such that more than one-third of patients older than 80 years in sinus rhythm received single-chamber ventricular pacing. The remote monitoring program included 35% of conventional pacemakers and 55% of low-energy resynchronization pacemakers. Conclusions: The number of conventional pacemakers increased by 5.6%, low-energy resynchronizers by 16%, and leadless pacemakers by 25%. Adherence to remote monitoring was stable. The number of procedures included in CardioDispositivos increased by 11%, although the sample volume decreased. In the coming years, the widespread use of the platform will likely lead to a high-quality registry.(AU)

Cost-effectiveness of remote patient monitoring for First Nations peoples living with diabetes in regional Australia.

The aim of this study was to determine the cost-effectiveness of remote patient monitoring (RPM) with First Nations peoples living with diabetes. This study was set at the Goondir Health Service (GHS), an Aboriginal and Torres Strait Islander Community-Controlled Health in South-West Queensland. Electronic medical records and RPM data were provided by the GHS. Clinical effectiveness was determined by comparing mean HbA1c before and after enrolment in the RPM service. Our analysis found no statistically significant effect between the mean HbA1c before and after enrolment, so this analysis focused on net-benefit and return on investment for costs from the perspective of the GHS. The 6-month RPM service for 84 clients cost AUD $67,841 to cover RPM equipment, ongoing technology costs, and a dedicated Virtual Care Manager, equating to $808 per client. There were 199 additional client-clinician interactions in the period after enrolment resulting in an additional $4797 revenue for the GHS. Therefore, the program cost the GHS $63,044 to deliver, representing a return on investment of around 7 cents for every dollar they spent. Whilst the diabetes RPM service was equally effective as usual care and resulted in increased interactions with clients, the cost for the service was substantially more than the additional revenue generated from increased interactions. This evidence highlights the need for alternative funding models for RPM services and demonstrates the need to focus future research on long-term clinical effects and the extra-clinical benefits resulting from services of this type.

How often do healthy people cough?

Objective cough frequency has been reported in several respiratory conditions but the amount that healthy individuals cough daily is unclear. Seventy-nine healthy volunteers (38 males, median [IQR] age 41y [IQR 30-53]) completed 24-hour ambulatory cough monitoring (VitaloJAK™). The audio recording was filtered using a custom written algorithm to remove non-cough sounds and then all individual explosive cough sounds in the filtered file were tagged electronically by trained cough counters. Most coughing occurred during the day and cough numbers over 24 h were generally low (geometric mean of 4.6 coughs) but there was large variability; ranging from 0 to 136 coughs overall. Cough frequency was independent of participant characteristics apart from sex with males coughing significantly, 4-5 fold, more than females during the day and over 24 h (median [IQR] 16.1 [3.8-33.4] vs. 4.1 [1.0-15.0] total coughs; p = 0.015). This is the first report to describe cough frequency in a balanced group of healthy adults using an accurate cough monitoring system. The data reveal a further example of sexual dimorphism in cough, which warrants additional investigation.

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers.

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.